#include <HybridGaussianFactorGraph.h>

Inheritance diagram for gtsam::HybridGaussianFactorGraph:

Collaboration diagram for gtsam::HybridGaussianFactorGraph:

Public Types
using	Base = HybridFactorGraph

using	This = HybridGaussianFactorGraph
	this class

using	BaseEliminateable = EliminateableFactorGraph< This >
	for elimination

using	shared_ptr = std::shared_ptr< This >
	shared_ptr to This

using	Values = gtsam::Values
	backwards compatibility

using	Indices = KeyVector
	map from keys to values

typedef Factor	FactorType
	factor type

typedef std::shared_ptr< Factor >	sharedFactor
	Shared pointer to a factor.

typedef sharedFactor	value_type

typedef FastVector< sharedFactor >::iterator	iterator

typedef FastVector< sharedFactor >::const_iterator	const_iterator

typedef EliminationTraits< FactorGraphType >	EliminationTraitsType
	Typedef to the specific EliminationTraits for this graph.

typedef EliminationTraitsType::ConditionalType	ConditionalType
	Conditional type stored in the Bayes net produced by elimination.

typedef EliminationTraitsType::BayesNetType	BayesNetType
	Bayes net type produced by sequential elimination.

typedef EliminationTraitsType::EliminationTreeType	EliminationTreeType
	Elimination tree type that can do sequential elimination of this graph.

typedef EliminationTraitsType::BayesTreeType	BayesTreeType
	Bayes tree type produced by multifrontal elimination.

typedef EliminationTraitsType::JunctionTreeType	JunctionTreeType
	Junction tree type that can do multifrontal elimination of this graph.

typedef std::pair< std::shared_ptr< ConditionalType >, std::shared_ptr< _FactorType > >	EliminationResult

typedef std::function< EliminationResult(const FactorGraphType &, const Ordering &)>	Eliminate
	The function type that does a single dense elimination step on a subgraph.

typedef std::optional< std::reference_wrapper< const VariableIndex > >	OptionalVariableIndex

typedef std::optional< Ordering::OrderingType >	OptionalOrderingType
	Typedef for an optional ordering type.

Public Member Functions
std::shared_ptr< BayesNetType >	eliminateSequential (OptionalOrderingType orderingType={}, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesNetType >	eliminateSequential (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesTreeType >	eliminateMultifrontal (OptionalOrderingType orderingType={}, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesTreeType >	eliminateMultifrontal (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::pair< std::shared_ptr< BayesNetType >, std::shared_ptr< FactorGraphType > >	eliminatePartialSequential (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::pair< std::shared_ptr< BayesNetType >, std::shared_ptr< FactorGraphType > >	eliminatePartialSequential (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::pair< std::shared_ptr< BayesTreeType >, std::shared_ptr< FactorGraphType > >	eliminatePartialMultifrontal (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::pair< std::shared_ptr< BayesTreeType >, std::shared_ptr< FactorGraphType > >	eliminatePartialMultifrontal (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesNetType >	marginalMultifrontalBayesNet (const Ordering &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesNetType >	marginalMultifrontalBayesNet (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesNetType >	marginalMultifrontalBayesNet (const Ordering &variables, const Ordering &marginalizedVariableOrdering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesNetType >	marginalMultifrontalBayesNet (const KeyVector &variables, const Ordering &marginalizedVariableOrdering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesTreeType >	marginalMultifrontalBayesTree (const Ordering &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesTreeType >	marginalMultifrontalBayesTree (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesTreeType >	marginalMultifrontalBayesTree (const Ordering &variables, const Ordering &marginalizedVariableOrdering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesTreeType >	marginalMultifrontalBayesTree (const KeyVector &variables, const Ordering &marginalizedVariableOrdering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< FactorGraphType >	marginal (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

Constructors
	HybridGaussianFactorGraph ()=default
	Default constructor.

template<class DERIVEDFACTOR >
	HybridGaussianFactorGraph (const FactorGraph< DERIVEDFACTOR > &graph)

Standard Interface
AlgebraicDecisionTree< Key >	error (const VectorValues &continuousValues) const
	Compute error for each discrete assignment, and return as a tree. More...

AlgebraicDecisionTree< Key >	probPrime (const VectorValues &continuousValues) const
	Compute unnormalized probability \( P(X \| M, Z) \) for each discrete assignment, and return as a tree. More...

double	probPrime (const HybridValues &values) const
	Compute the unnormalized posterior probability for a continuous vector values given a specific assignment. More...

GaussianFactorGraphTree	assembleGraphTree () const
	Create a decision tree of factor graphs out of this hybrid factor graph. More...

Extra methods to inspect discrete/continuous keys.
std::set< DiscreteKey >	discreteKeys () const
	Get all the discrete keys in the factor graph.

KeySet	discreteKeySet () const
	Get all the discrete keys in the factor graph, as a set.

std::unordered_map< Key, DiscreteKey >	discreteKeyMap () const
	Get a map from Key to corresponding DiscreteKey.

const KeySet	continuousKeySet () const
	Get all the continuous keys in the factor graph.

Adding Single Factors
void	reserve (size_t size)

IsDerived< DERIVEDFACTOR >	push_back (std::shared_ptr< DERIVEDFACTOR > factor)
	Add a factor directly using a shared_ptr.

IsDerived< DERIVEDFACTOR >	push_back (const DERIVEDFACTOR &factor)

IsDerived< DERIVEDFACTOR >	emplace_shared (Args &&... args)
	Emplace a shared pointer to factor of given type.

IsDerived< DERIVEDFACTOR >	add (std::shared_ptr< DERIVEDFACTOR > factor)
	`add` is a synonym for push_back.

Adding via iterators
HasDerivedElementType< ITERATOR >	push_back (ITERATOR firstFactor, ITERATOR lastFactor)

HasDerivedValueType< ITERATOR >	push_back (ITERATOR firstFactor, ITERATOR lastFactor)
	Push back many factors with an iterator (factors are copied)

Adding via container
HasDerivedElementType< CONTAINER >	push_back (const CONTAINER &container)

HasDerivedValueType< CONTAINER >	push_back (const CONTAINER &container)
	Push back non-pointer objects in a container (factors are copied).

void	add (const FACTOR_OR_CONTAINER &factorOrContainer)

Specialized versions
std::enable_if< std::is_base_of< This, typename CLIQUE::FactorGraphType >::value >::type	push_back (const BayesTree< CLIQUE > &bayesTree)

FactorIndices	add_factors (const CONTAINER &factors, bool useEmptySlots=false)

Testable
virtual void	print (const std::string &s="FactorGraph", const KeyFormatter &formatter=DefaultKeyFormatter) const
	Print out graph to std::cout, with optional key formatter.

bool	equals (const This &fg, double tol=1e-9) const
	Check equality up to tolerance.

Standard Interface
size_t	size () const

bool	empty () const

const sharedFactor	at (size_t i) const

sharedFactor &	at (size_t i)

const sharedFactor	operator[] (size_t i) const

sharedFactor &	operator[] (size_t i)

const_iterator	begin () const

const_iterator	end () const

sharedFactor	front () const

sharedFactor	back () const

double	error (const HybridValues &values) const

Modifying Factor Graphs (imperative, discouraged)
iterator	begin ()

iterator	end ()

virtual void	resize (size_t size)

void	remove (size_t i)

void	replace (size_t index, sharedFactor factor)

iterator	erase (iterator item)

iterator	erase (iterator first, iterator last)

Graph Display
void	dot (std::ostream &os, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const
	Output to graphviz format, stream version.

std::string	dot (const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const
	Output to graphviz format string.

void	saveGraph (const std::string &filename, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const
	output to file with graphviz format.

Advanced Interface
size_t	nrFactors () const

KeySet	keys () const

KeyVector	keyVector () const

bool	exists (size_t idx) const

Protected Types
template<typename FACTOR >
using	IsGaussian = typename std::enable_if< std::is_base_of< GaussianFactor, FACTOR >::value >::type
	Check if FACTOR type is derived from GaussianFactor.

Protected Member Functions
bool	isEqual (const FactorGraph &other) const
	Check exact equality of the factor pointers. Useful for derived ==.

Protected Attributes
FastVector< sharedFactor >	factors_

Detailed Description

This is the linearized version of a hybrid factor graph.

Member Typedef Documentation

◆ EliminationResult

typedef std::pair<std::shared_ptr<ConditionalType>, std::shared_ptr<_FactorType> > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::EliminationResult

inherited

The pair of conditional and remaining factor produced by a single dense elimination step on a subgraph.

◆ OptionalVariableIndex

typedef std::optional<std::reference_wrapper<const VariableIndex> > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::OptionalVariableIndex

inherited

Typedef for an optional variable index as an argument to elimination functions It is an optional to a constant reference

Constructor & Destructor Documentation

◆ HybridGaussianFactorGraph()

template<class DERIVEDFACTOR >

gtsam::HybridGaussianFactorGraph::HybridGaussianFactorGraph ( const FactorGraph< DERIVEDFACTOR > & graph )

inline

Implicit copy/downcast constructor to override explicit template container constructor. In BayesTree this is used for: cachedSeparatorMarginal_.reset(*separatorMarginal)

Member Function Documentation

◆ add()

void gtsam::FactorGraph< Factor >::add ( const FACTOR_OR_CONTAINER & factorOrContainer )

inlineinherited

Add a factor or container of factors, including STL collections, BayesTrees, etc.

◆ add_factors()

FactorIndices gtsam::FactorGraph< Factor >::add_factors	(	const CONTAINER &	factors,
		bool	useEmptySlots = `false`
	)

inherited

Add new factors to a factor graph and returns a list of new factor indices, optionally finding and reusing empty factor slots.

◆ assembleGraphTree()

GaussianFactorGraphTree gtsam::HybridGaussianFactorGraph::assembleGraphTree ( ) const

Create a decision tree of factor graphs out of this hybrid factor graph.

For example, if there are two mixture factors, one with a discrete key A and one with a discrete key B, then the decision tree will have two levels, one for A and one for B. The leaves of the tree will be the Gaussian factors that have only continuous keys.

◆ at() [1/2]

const sharedFactor gtsam::FactorGraph< Factor >::at ( size_t i ) const

inlineinherited

Get a specific factor by index (this checks array bounds and may throw an exception, as opposed to operator[] which does not).

◆ at() [2/2]

sharedFactor& gtsam::FactorGraph< Factor >::at ( size_t i )

inlineinherited

Get a specific factor by index (this checks array bounds and may throw an exception, as opposed to operator[] which does not).

◆ back()

sharedFactor gtsam::FactorGraph< Factor >::back ( ) const

inlineinherited

Get the last factor

◆ begin() [1/2]

const_iterator gtsam::FactorGraph< Factor >::begin ( ) const

inlineinherited

Iterator to beginning of factors.

◆ begin() [2/2]

iterator gtsam::FactorGraph< Factor >::begin ( )

inlineinherited

non-const STL-style begin()

◆ eliminateMultifrontal() [1/2]

std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesTreeType > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::eliminateMultifrontal	(	OptionalOrderingType	orderingType = `{}`,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do multifrontal elimination of all variables to produce a Bayes tree. If an ordering is not provided, the ordering will be computed using either COLAMD or METIS, depending on the parameter orderingType (Ordering::COLAMD or Ordering::METIS)

Example - Full Cholesky elimination in COLAMD order:

std::shared_ptr<GaussianBayesTree> result = graph.eliminateMultifrontal(EliminateCholesky);

Example - Reusing an existing VariableIndex to improve performance, and using COLAMD ordering:

VariableIndex varIndex(graph); // Build variable index
Data data = otherFunctionUsingVariableIndex(graph, varIndex); // Other code that uses variable index
std::shared_ptr<GaussianBayesTree> result = graph.eliminateMultifrontal(EliminateQR, {}, varIndex);

◆ eliminateMultifrontal() [2/2]

std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesTreeType > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::eliminateMultifrontal	(	const Ordering &	ordering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do multifrontal elimination of all variables to produce a Bayes tree. If an ordering is not provided, the ordering will be computed using either COLAMD or METIS, depending on the parameter orderingType (Ordering::COLAMD or Ordering::METIS)

Example - Full QR elimination in specified order:

std::shared_ptr<GaussianBayesTree> result = graph.eliminateMultifrontal(EliminateQR, myOrdering);

◆ eliminatePartialMultifrontal() [1/2]

std::pair< std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesTreeType >, std::shared_ptr< HybridGaussianFactorGraph > > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::eliminatePartialMultifrontal	(	const Ordering &	ordering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do multifrontal elimination of some variables, in ordering provided, to produce a Bayes tree and a remaining factor graph. This computes the factorization \( p(X) = p(A|B) p(B) \), where \( A = \) variables, \( X \) is all the variables in the factor graph, and \( B = X\backslash A \).

◆ eliminatePartialMultifrontal() [2/2]

std::pair< std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesTreeType >, std::shared_ptr< HybridGaussianFactorGraph > > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::eliminatePartialMultifrontal	(	const KeyVector &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do multifrontal elimination of the given variables in an ordering computed by COLAMD to produce a Bayes tree and a remaining factor graph. This computes the factorization \( p(X) = p(A|B) p(B) \), where \( A = \) variables, \( X \) is all the variables in the factor graph, and \( B = X\backslash A \).

◆ eliminatePartialSequential() [1/2]

std::pair< std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesNetType >, std::shared_ptr< HybridGaussianFactorGraph > > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::eliminatePartialSequential	(	const Ordering &	ordering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do sequential elimination of some variables, in ordering provided, to produce a Bayes net and a remaining factor graph. This computes the factorization \( p(X) = p(A|B) p(B) \), where \( A = \) variables, \( X \) is all the variables in the factor graph, and \( B = X\backslash A \).

◆ eliminatePartialSequential() [2/2]

std::pair< std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesNetType >, std::shared_ptr< HybridGaussianFactorGraph > > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::eliminatePartialSequential	(	const KeyVector &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do sequential elimination of the given variables in an ordering computed by COLAMD to produce a Bayes net and a remaining factor graph. This computes the factorization \( p(X) = p(A|B) p(B) \), where \( A = \) variables, \( X \) is all the variables in the factor graph, and \( B = X\backslash A \).

◆ eliminateSequential() [1/2]

std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesNetType > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::eliminateSequential	(	OptionalOrderingType	orderingType = `{}`,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do sequential elimination of all variables to produce a Bayes net. If an ordering is not provided, the ordering provided by COLAMD will be used.

Example - Full Cholesky elimination in COLAMD order:

std::shared_ptr<GaussianBayesNet> result = graph.eliminateSequential(EliminateCholesky);

Example - METIS ordering for elimination

std::shared_ptr<GaussianBayesNet> result = graph.eliminateSequential(OrderingType::METIS);

Example - Reusing an existing VariableIndex to improve performance, and using COLAMD ordering:

VariableIndex varIndex(graph); // Build variable index
Data data = otherFunctionUsingVariableIndex(graph, varIndex); // Other code that uses variable index
std::shared_ptr<GaussianBayesNet> result = graph.eliminateSequential(EliminateQR, varIndex, std::nullopt);

◆ eliminateSequential() [2/2]

std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesNetType > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::eliminateSequential	(	const Ordering &	ordering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do sequential elimination of all variables to produce a Bayes net.

Example - Full QR elimination in specified order:

std::shared_ptr<GaussianBayesNet> result = graph.eliminateSequential(myOrdering, EliminateQR);

Example - Reusing an existing VariableIndex to improve performance:

VariableIndex varIndex(graph); // Build variable index
Data data = otherFunctionUsingVariableIndex(graph, varIndex); // Other code that uses variable index
std::shared_ptr<GaussianBayesNet> result = graph.eliminateSequential(myOrdering, EliminateQR, varIndex, std::nullopt);

◆ empty()

bool gtsam::FactorGraph< Factor >::empty ( ) const

inlineinherited

Check if the graph is empty (null factors set by remove() will cause this to return false).

◆ end() [1/2]

const_iterator gtsam::FactorGraph< Factor >::end ( ) const

inlineinherited

Iterator to end of factors.

◆ end() [2/2]

iterator gtsam::FactorGraph< Factor >::end ( )

inlineinherited

non-const STL-style end()

◆ erase() [1/2]

iterator gtsam::FactorGraph< Factor >::erase ( iterator item )

inlineinherited

Erase factor and rearrange other factors to take up the empty space

◆ erase() [2/2]

iterator gtsam::FactorGraph< Factor >::erase	(	iterator	first,
		iterator	last
	)

inlineinherited

Erase factors and rearrange other factors to take up the empty space

◆ error() [1/2]

AlgebraicDecisionTree<Key> gtsam::HybridGaussianFactorGraph::error ( const VectorValues & continuousValues ) const

Compute error for each discrete assignment, and return as a tree.

Error \( e = \Vert x - \mu \Vert_{\Sigma} \).

Parameters

continuousValues Continuous values at which to compute the error.

Returns: AlgebraicDecisionTree<Key>

◆ error() [2/2]

double gtsam::FactorGraph< Factor >::error ( const HybridValues & values ) const

inherited

Add error for all factors.

◆ exists()

bool gtsam::FactorGraph< Factor >::exists ( size_t idx ) const

inlineinherited

MATLAB interface utility: Checks whether a factor index idx exists in the graph and is a live pointer

◆ front()

sharedFactor gtsam::FactorGraph< Factor >::front ( ) const

inlineinherited

Get the first factor

◆ keys()

KeySet gtsam::FactorGraph< Factor >::keys ( ) const

inherited

Potentially slow function to return all keys involved, sorted, as a set

◆ keyVector()

KeyVector gtsam::FactorGraph< Factor >::keyVector ( ) const

inherited

Potentially slow function to return all keys involved, sorted, as a vector

◆ marginal()

std::shared_ptr< HybridGaussianFactorGraph > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::marginal	(	const KeyVector &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal factor graph of the requested variables.

◆ marginalMultifrontalBayesNet() [1/4]

std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesNetType > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::marginalMultifrontalBayesNet	(	const Ordering &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes net. Uses COLAMD marginalization ordering by default

Parameters

variables	Determines the ordered variables whose marginal to compute, will be ordered in the returned BayesNet as specified.
function	Optional dense elimination function.
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesNet() [2/4]

std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesNetType > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::marginalMultifrontalBayesNet	(	const KeyVector &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes net. Uses COLAMD marginalization ordering by default

Parameters

variables	Determines the variables whose marginal to compute, will be ordered using COLAMD; use `Ordering(variables)` to specify the variable ordering.
function	Optional dense elimination function.
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesNet() [3/4]

std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesNetType > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::marginalMultifrontalBayesNet	(	const Ordering &	variables,
		const Ordering &	marginalizedVariableOrdering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes net.

Parameters

variables	Determines the ordered variables whose marginal to compute, will be ordered in the returned BayesNet as specified.
marginalizedVariableOrdering	Ordering for the variables being marginalized out, i.e. all variables not in `variables`.
function	Optional dense elimination function.
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesNet() [4/4]

std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesNetType > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::marginalMultifrontalBayesNet	(	const KeyVector &	variables,
		const Ordering &	marginalizedVariableOrdering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes net.

Parameters

variables	Determines the variables whose marginal to compute, will be ordered using COLAMD; use `Ordering(variables)` to specify the variable ordering.
marginalizedVariableOrdering	Ordering for the variables being marginalized out, i.e. all variables not in `variables`.
function	Optional dense elimination function.
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesTree() [1/4]

std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesTreeType > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::marginalMultifrontalBayesTree	(	const Ordering &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes tree. Uses COLAMD marginalization order by default

Parameters

variables	Determines the ordered variables whose marginal to compute, will be ordered in the returned BayesNet as specified.
function	Optional dense elimination function..
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesTree() [2/4]

std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesTreeType > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::marginalMultifrontalBayesTree	(	const KeyVector &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes tree. Uses COLAMD marginalization order by default

Parameters

variables	Determines the variables whose marginal to compute, will be ordered using COLAMD; use `Ordering(variables)` to specify the variable ordering.
function	Optional dense elimination function..
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesTree() [3/4]

std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesTreeType > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::marginalMultifrontalBayesTree	(	const Ordering &	variables,
		const Ordering &	marginalizedVariableOrdering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes tree.

Parameters

variables	Determines the ordered variables whose marginal to compute, will be ordered in the returned BayesNet as specified.
marginalizedVariableOrdering	Ordering for the variables being marginalized out, i.e. all variables not in `variables`.
function	Optional dense elimination function..
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesTree() [4/4]

std::shared_ptr< typename EliminateableFactorGraph< HybridGaussianFactorGraph >::BayesTreeType > gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >::marginalMultifrontalBayesTree	(	const KeyVector &	variables,
		const Ordering &	marginalizedVariableOrdering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes tree.

Parameters

variables	Determines the variables whose marginal to compute, will be ordered using COLAMD; use `Ordering(variables)` to specify the variable ordering.
marginalizedVariableOrdering	Ordering for the variables being marginalized out, i.e. all variables not in `variables`.
function	Optional dense elimination function..
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ nrFactors()

size_t gtsam::FactorGraph< Factor >::nrFactors ( ) const

inherited

return the number of non-null factors

◆ operator[]() [1/2]

const sharedFactor gtsam::FactorGraph< Factor >::operator[] ( size_t i ) const

inlineinherited

Get a specific factor by index (this does not check array bounds, as opposed to at() which does).

◆ operator[]() [2/2]

sharedFactor& gtsam::FactorGraph< Factor >::operator[] ( size_t i )

inlineinherited

Get a specific factor by index (this does not check array bounds, as opposed to at() which does).

◆ probPrime() [1/2]

AlgebraicDecisionTree<Key> gtsam::HybridGaussianFactorGraph::probPrime ( const VectorValues & continuousValues ) const

Compute unnormalized probability \( P(X | M, Z) \) for each discrete assignment, and return as a tree.

Parameters

continuousValues Continuous values at which to compute the probability.

Returns: AlgebraicDecisionTree<Key>

◆ probPrime() [2/2]

double gtsam::HybridGaussianFactorGraph::probPrime ( const HybridValues & values ) const

Compute the unnormalized posterior probability for a continuous vector values given a specific assignment.

Returns: double

◆ push_back() [1/4]

IsDerived<DERIVEDFACTOR> gtsam::FactorGraph< Factor >::push_back ( const DERIVEDFACTOR & factor )

inlineinherited

Add a factor by value, will be copy-constructed (use push_back with a shared_ptr to avoid the copy).

◆ push_back() [2/4]

HasDerivedElementType<ITERATOR> gtsam::FactorGraph< Factor >::push_back	(	ITERATOR	firstFactor,
		ITERATOR	lastFactor
	)

inlineinherited

Push back many factors with an iterator over shared_ptr (factors are not copied)

◆ push_back() [3/4]

HasDerivedElementType<CONTAINER> gtsam::FactorGraph< Factor >::push_back ( const CONTAINER & container )

inlineinherited

Push back many factors as shared_ptr's in a container (factors are not copied)

◆ push_back() [4/4]

std::enable_if< std::is_base_of<This, typename CLIQUE::FactorGraphType>::value>::type gtsam::FactorGraph< Factor >::push_back ( const BayesTree< CLIQUE > & bayesTree )

inlineinherited

Push back a BayesTree as a collection of factors. NOTE: This should be hidden in derived classes in favor of a type-specialized version that calls this templated function.

◆ remove()

void gtsam::FactorGraph< Factor >::remove ( size_t i )

inlineinherited

delete factor without re-arranging indexes by inserting a nullptr pointer

◆ replace()

void gtsam::FactorGraph< Factor >::replace	(	size_t	index,
		sharedFactor	factor
	)

inlineinherited

replace a factor by index

◆ reserve()

void gtsam::FactorGraph< Factor >::reserve ( size_t size )

inlineinherited

Reserve space for the specified number of factors if you know in advance how many there will be (works like FastVector::reserve).

◆ resize()

virtual void gtsam::FactorGraph< Factor >::resize ( size_t size )

inlinevirtualinherited

Directly resize the number of factors in the graph. If the new size is less than the original, factors at the end will be removed. If the new size is larger than the original, null factors will be appended.

◆ size()

size_t gtsam::FactorGraph< Factor >::size ( ) const

inlineinherited

return the number of factors (including any null factors set by remove() ).

Member Data Documentation

◆ factors_

FastVector<sharedFactor> gtsam::FactorGraph< Factor >::factors_

protectedinherited

concept check, makes sure FACTOR defines print and equals Collection of factors

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/HybridGaussianFactorGraph.h

Public Types

Public Member Functions

Protected Types

Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

◆ EliminationResult

◆ OptionalVariableIndex

Constructor & Destructor Documentation

◆ HybridGaussianFactorGraph()

Member Function Documentation

◆ add()

◆ add_factors()

◆ assembleGraphTree()

◆ at() [1/2]

◆ at() [2/2]

◆ back()

◆ begin() [1/2]

◆ begin() [2/2]

◆ eliminateMultifrontal() [1/2]

◆ eliminateMultifrontal() [2/2]

◆ eliminatePartialMultifrontal() [1/2]

◆ eliminatePartialMultifrontal() [2/2]

◆ eliminatePartialSequential() [1/2]

◆ eliminatePartialSequential() [2/2]

◆ eliminateSequential() [1/2]

◆ eliminateSequential() [2/2]

◆ empty()

◆ end() [1/2]

◆ end() [2/2]

◆ erase() [1/2]

◆ erase() [2/2]

◆ error() [1/2]

◆ error() [2/2]

◆ exists()

◆ front()

◆ keys()

◆ keyVector()

◆ marginal()

◆ marginalMultifrontalBayesNet() [1/4]

◆ marginalMultifrontalBayesNet() [2/4]

◆ marginalMultifrontalBayesNet() [3/4]

◆ marginalMultifrontalBayesNet() [4/4]

◆ marginalMultifrontalBayesTree() [1/4]

◆ marginalMultifrontalBayesTree() [2/4]

◆ marginalMultifrontalBayesTree() [3/4]

◆ marginalMultifrontalBayesTree() [4/4]

◆ nrFactors()

◆ operator[]() [1/2]

◆ operator[]() [2/2]

◆ probPrime() [1/2]

◆ probPrime() [2/2]

◆ push_back() [1/4]

◆ push_back() [2/4]

◆ push_back() [3/4]

◆ push_back() [4/4]

◆ remove()

◆ replace()

◆ reserve()

◆ resize()

◆ size()

Member Data Documentation

◆ factors_