#include <HybridGaussianFactorGraph.h>

Inheritance diagram for gtsam::HybridGaussianFactorGraph:

[legend]

Public Types
using	Base = HybridFactorGraph

using	BaseEliminateable = EliminateableFactorGraph< This >

using	Indices = KeyVector
	map from keys to values More...

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

using	This = HybridGaussianFactorGraph
	for elimination More...

using	Values = gtsam::Values
	backwards compatibility More...

Public Types inherited from gtsam::HybridFactorGraph
using	Base = FactorGraph< Factor >

using	Indices = KeyVector

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

using	This = HybridFactorGraph
	this class More...

using	Values = gtsam::Values
	backwards compatibility More...

Public Types inherited from gtsam::FactorGraph< Factor >
typedef FastVector< sharedFactor >::const_iterator	const_iterator

typedef Factor	FactorType
	factor type More...

typedef FastVector< sharedFactor >::iterator	iterator

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

typedef sharedFactor	value_type

Public Types inherited from gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >
typedef EliminationTraitsType::BayesNetType	BayesNetType
	Bayes net type produced by sequential elimination. More...

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

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

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

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

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

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

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

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

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

Public Member Functions
GaussianFactorGraph	choose (const DiscreteValues &assignment) const
	Get the GaussianFactorGraph at a given discrete assignment. Note this corresponds to the Gaussian posterior p(X\|M=m, Z=z) of the continuous variables X given the discrete assignment M=m and whatever measurements z where assumed in the creation of the factor Graph. More...

DiscreteFactorGraph	discreteFactors () const
	Helper method to get all the discrete factors as a DiscreteFactorGraph. More...

GaussianFactorGraph	operator() (const DiscreteValues &assignment) const
	Syntactic sugar for choose. More...

Constructors
	HybridGaussianFactorGraph ()=default
	Default constructor. More...

template<class CONTAINER >
	HybridGaussianFactorGraph (const CONTAINER &factors)

	HybridGaussianFactorGraph (std::initializer_list< sharedFactor > factors)

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

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

void	printErrors (const HybridValues &values, const std::string &str="HybridGaussianFactorGraph: ", const KeyFormatter &keyFormatter=DefaultKeyFormatter, const std::function< bool(const Factor , double, size_t)> &printCondition=[](const Factor , double, size_t) { return true;}) const
	Print the errors of each factor in the hybrid factor graph. More...

Standard Interface
AlgebraicDecisionTree< Key >	errorTree (const VectorValues &continuousValues) const
	Compute error 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...

AlgebraicDecisionTree< Key >	discretePosterior (const VectorValues &continuousValues) const
	Computer posterior P(M\|X=x) when all continuous values X are given. This is efficient as this simply probPrime normalized. More...

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

std::pair< std::shared_ptr< HybridConditional >, std::shared_ptr< Factor > >	eliminate (const Ordering &keys) const
	Eliminate the given continuous keys. More...

Public Member Functions inherited from gtsam::HybridFactorGraph
	HybridFactorGraph ()=default
	Default constructor. More...

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

template<class CONTAINER >
	HybridFactorGraph (const CONTAINER &factors)

std::set< DiscreteKey >	discreteKeys () const
	Get all the discrete keys in the factor graph. More...

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

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

Public Member Functions inherited from gtsam::FactorGraph< Factor >
	FactorGraph (std::initializer_list< std::shared_ptr< DERIVEDFACTOR >> sharedFactors)

virtual	~FactorGraph ()=default

void	reserve (size_t size)

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

IsDerived< DERIVEDFACTOR >	push_back (const DERIVEDFACTOR &factor)

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

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

std::enable_if< std::is_base_of< FactorType, DERIVEDFACTOR >::value, This >::type &	operator+= (std::shared_ptr< DERIVEDFACTOR > factor)
	Append factor to factor graph. More...

std::enable_if< std::is_base_of< FactorType, DERIVEDFACTOR >::value, This >::type &	operator, (std::shared_ptr< DERIVEDFACTOR > factor)
	Overload comma operator to allow for append chaining. More...

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) More...

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). More...

void	add (const FACTOR_OR_CONTAINER &factorOrContainer)

This &	operator+= (const FACTOR_OR_CONTAINER &factorOrContainer)

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)

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

size_t	size () const

bool	empty () const

const sharedFactor	at (size_t i) const

sharedFactor &	at (size_t i)

std::shared_ptr< F >	at (size_t i)

const std::shared_ptr< F >	at (size_t i) const
	Const version of templated `at` method. More...

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

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)

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

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

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

size_t	nrFactors () const

KeySet	keys () const

KeyVector	keyVector () const

bool	exists (size_t idx) const

Public Member Functions inherited from gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >
std::shared_ptr< BayesTreeType >	eliminateMultifrontal (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::pair< std::shared_ptr< BayesTreeType >, std::shared_ptr< FactorGraphType > >	eliminatePartialMultifrontal (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< BayesNetType >, std::shared_ptr< FactorGraphType > >	eliminatePartialSequential (const KeyVector &variables, 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::shared_ptr< BayesNetType >	eliminateSequential (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

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

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

Additional Inherited Members
Protected Member Functions inherited from gtsam::FactorGraph< Factor >
bool	isEqual (const FactorGraph &other) const
	Check exact equality of the factor pointers. Useful for derived ==. More...

	FactorGraph ()

	FactorGraph (ITERATOR firstFactor, ITERATOR lastFactor)

	FactorGraph (const CONTAINER &factors)

Protected Attributes inherited from gtsam::FactorGraph< Factor >
FastVector< sharedFactor >	factors_

Detailed Description

Hybrid Gaussian Factor Graph

This is the linearized version of a hybrid factor graph.

Definition at line 106 of file HybridGaussianFactorGraph.h.

Member Typedef Documentation

◆ Base

using gtsam::HybridGaussianFactorGraph::Base = HybridFactorGraph

Definition at line 116 of file HybridGaussianFactorGraph.h.

◆ BaseEliminateable

using gtsam::HybridGaussianFactorGraph::BaseEliminateable = EliminateableFactorGraph<This>

Definition at line 119 of file HybridGaussianFactorGraph.h.

◆ Indices

using gtsam::HybridGaussianFactorGraph::Indices = KeyVector

map from keys to values

Definition at line 123 of file HybridGaussianFactorGraph.h.

◆ IsGaussian

template<typename FACTOR >

using gtsam::HybridGaussianFactorGraph::IsGaussian = typename std::enable_if< std::is_base_of<GaussianFactor, FACTOR>::value>::type

protected

Check if FACTOR type is derived from GaussianFactor.

Definition at line 113 of file HybridGaussianFactorGraph.h.

◆ shared_ptr

using gtsam::HybridGaussianFactorGraph::shared_ptr = std::shared_ptr<This>

shared_ptr to This

Definition at line 120 of file HybridGaussianFactorGraph.h.

◆ This

using gtsam::HybridGaussianFactorGraph::This = HybridGaussianFactorGraph

for elimination

Definition at line 118 of file HybridGaussianFactorGraph.h.

◆ Values

using gtsam::HybridGaussianFactorGraph::Values = gtsam::Values

backwards compatibility

Definition at line 122 of file HybridGaussianFactorGraph.h.

Constructor & Destructor Documentation

◆ HybridGaussianFactorGraph() [1/4]

gtsam::HybridGaussianFactorGraph::HybridGaussianFactorGraph ( )

default

Default constructor.

◆ HybridGaussianFactorGraph() [2/4]

template<class CONTAINER >

gtsam::HybridGaussianFactorGraph::HybridGaussianFactorGraph ( const CONTAINER & factors )

inlineexplicit

Construct from container of factors (shared_ptr or plain objects)

Definition at line 133 of file HybridGaussianFactorGraph.h.

◆ HybridGaussianFactorGraph() [3/4]

gtsam::HybridGaussianFactorGraph::HybridGaussianFactorGraph ( std::initializer_list< sharedFactor > factors )

inline

Construct from an initializer lists of GaussianFactor shared pointers. Example: HybridGaussianFactorGraph graph = { factor1, factor2, factor3 };

Definition at line 141 of file HybridGaussianFactorGraph.h.

◆ HybridGaussianFactorGraph() [4/4]

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)

Definition at line 150 of file HybridGaussianFactorGraph.h.

Member Function Documentation

◆ choose()

GaussianFactorGraph gtsam::HybridGaussianFactorGraph::choose ( const DiscreteValues & assignment ) const

Get the GaussianFactorGraph at a given discrete assignment. Note this corresponds to the Gaussian posterior p(X|M=m, Z=z) of the continuous variables X given the discrete assignment M=m and whatever measurements z where assumed in the creation of the factor Graph.

Note: Be careful, as any factors not Gaussian are ignored.

Parameters

assignment The discrete value assignment for the discrete keys.

Returns: Gaussian factors as a GaussianFactorGraph

Definition at line 560 of file HybridGaussianFactorGraph.cpp.

◆ collectProductFactor()

HybridGaussianProductFactor gtsam::HybridGaussianFactorGraph::collectProductFactor ( ) const

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

For example, if there are two hybrid 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.

Definition at line 177 of file HybridGaussianFactorGraph.cpp.

◆ discreteFactors()

DiscreteFactorGraph gtsam::HybridGaussianFactorGraph::discreteFactors ( ) const

Helper method to get all the discrete factors as a DiscreteFactorGraph.

Returns: DiscreteFactorGraph

Definition at line 585 of file HybridGaussianFactorGraph.cpp.

◆ discretePosterior()

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

Computer posterior P(M|X=x) when all continuous values X are given. This is efficient as this simply probPrime normalized.

Note: Not a DiscreteConditional as the cardinalities of the DiscreteKeys, which we would need, are hard to recover.

Parameters

continuousValues Continuous values x to condition on.

Returns: DecisionTreeFactor

Definition at line 549 of file HybridGaussianFactorGraph.cpp.

◆ eliminate()

std::pair< HybridConditional::shared_ptr, std::shared_ptr< Factor > > gtsam::HybridGaussianFactorGraph::eliminate ( const Ordering & keys ) const

Eliminate the given continuous keys.

Parameters

keys	The continuous keys to eliminate.

Returns: The conditional on the keys and a factor on the separator.

<<<<<< MOST COMPUTE IS HERE

Definition at line 358 of file HybridGaussianFactorGraph.cpp.

◆ errorTree()

AlgebraicDecisionTree< Key > gtsam::HybridGaussianFactorGraph::errorTree ( 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>

Definition at line 520 of file HybridGaussianFactorGraph.cpp.

◆ operator()()

GaussianFactorGraph gtsam::HybridGaussianFactorGraph::operator() ( const DiscreteValues & assignment ) const

inline

Syntactic sugar for choose.

Definition at line 255 of file HybridGaussianFactorGraph.h.

◆ print()

void gtsam::HybridGaussianFactorGraph::print	(	const std::string &	s = `"HybridGaussianFactorGraph"`,
		const KeyFormatter &	formatter = `DefaultKeyFormatter`
	)		const

overridevirtual

Print out graph to std::cout, with optional key formatter.

Reimplemented from gtsam::FactorGraph< Factor >.

Definition at line 130 of file HybridGaussianFactorGraph.cpp.

◆ printErrors()

void gtsam::HybridGaussianFactorGraph::printErrors	(	const HybridValues &	values,
		const std::string &	str = `"HybridGaussianFactorGraph: "`,
		const KeyFormatter &	keyFormatter = `DefaultKeyFormatter`,
		const std::function< bool(const Factor *, double, size_t)> &	printCondition = `[](const Factor*, double, size_t) { return true; }`
	)		const

Print the errors of each factor in the hybrid factor graph.

Parameters

values	The HybridValues for the variables used to compute the error.
str	String that is output before the factor graph and errors.
keyFormatter	Formatter function for the keys in the factors.
printCondition	A condition to check if a factor should be printed.

Definition at line 150 of file HybridGaussianFactorGraph.cpp.

◆ probPrime()

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

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

Returns: double

Definition at line 542 of file HybridGaussianFactorGraph.cpp.

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

Public Types

Public Member Functions

Protected Types

Additional Inherited Members

Detailed Description

Hybrid Gaussian Factor Graph

Member Typedef Documentation

◆ Base

◆ BaseEliminateable

◆ Indices

◆ IsGaussian

◆ shared_ptr

◆ This

◆ Values

Constructor & Destructor Documentation

◆ HybridGaussianFactorGraph() [1/4]

◆ HybridGaussianFactorGraph() [2/4]

◆ HybridGaussianFactorGraph() [3/4]

◆ HybridGaussianFactorGraph() [4/4]

Member Function Documentation

◆ choose()

◆ collectProductFactor()

◆ discreteFactors()

◆ discretePosterior()

◆ eliminate()

◆ errorTree()

◆ operator()()

◆ print()

◆ printErrors()

◆ probPrime()