Detailed Description

A hybrid Bayes net is a collection of HybridConditionals, which can have discrete conditionals, Gaussian mixtures, or pure Gaussian conditionals.

Inheritance diagram for gtsam::HybridBayesNet:

Public Member Functions
Standard Constructors
	HybridBayesNet ()=default
	Construct empty Bayes net.
Testable
void	print (const std::string &s="", const KeyFormatter &formatter=DefaultKeyFormatter) const override
	GTSAM-style printing.
bool	equals (const This &fg, double tol=1e-9) const
	GTSAM-style equals.
Standard Interface
void	push_back (boost::shared_ptr< HybridConditional > conditional)
	Add a hybrid conditional using a shared_ptr.
template<class Conditional>
void	emplace_back (Conditional *conditional)
	Preferred: add a conditional directly using a pointer.
void	push_back (HybridConditional &&conditional)
	Add a conditional using a shared_ptr, using implicit conversion to a HybridConditional.
GaussianBayesNet	choose (const DiscreteValues &assignment) const
	Get the Gaussian Bayes Net which corresponds to a specific discrete value assignment.
double	evaluate (const HybridValues &values) const
	Evaluate hybrid probability density for given HybridValues.
double	operator() (const HybridValues &values) const
	Evaluate hybrid probability density for given HybridValues, sugar.
HybridValues	optimize () const
	Solve the HybridBayesNet by first computing the MPE of all the discrete variables and then optimizing the continuous variables based on the MPE assignment.
VectorValues	optimize (const DiscreteValues &assignment) const
	Given the discrete assignment, return the optimized estimate for the selected Gaussian BayesNet.
DecisionTreeFactor::shared_ptr	discreteConditionals () const
	Get all the discrete conditionals as a decision tree factor.
HybridValues	sample (const HybridValues &given, std::mt19937_64 *rng) const
	Sample from an incomplete BayesNet, given missing variables.
HybridValues	sample (std::mt19937_64 *rng) const
	Sample using ancestral sampling.
HybridValues	sample (const HybridValues &given) const
	Sample from an incomplete BayesNet, use default rng.
HybridValues	sample () const
	Sample using ancestral sampling, use default rng.
HybridBayesNet	prune (size_t maxNrLeaves)
	Prune the Hybrid Bayes Net such that we have at most maxNrLeaves leaves.
AlgebraicDecisionTree< Key >	logProbability (const VectorValues &continuousValues) const
	Compute conditional error for each discrete assignment, and return as a tree.
AlgebraicDecisionTree< Key >	evaluate (const VectorValues &continuousValues) const
	Compute unnormalized probability q(μ\|M), for each discrete assignment, and return as a tree.
HybridGaussianFactorGraph	toFactorGraph (const VectorValues &measurements) const
	Convert a hybrid Bayes net to a hybrid Gaussian factor graph by converting all conditionals with instantiated measurements into likelihood factors.
double	logProbability (const HybridValues &x) const
Public Member Functions inherited from gtsam::BayesNet< HybridConditional >
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.
double	logProbability (const HybridValues &x) const
double	evaluate (const HybridValues &c) const
size_t	nrFactors () const
	return the number of non-null factors
KeySet	keys () const
	Potentially slow function to return all keys involved, sorted, as a set.
KeyVector	keyVector () const
	Potentially slow function to return all keys involved, sorted, as a vector.
bool	exists (size_t idx) const
	MATLAB interface utility: Checks whether a factor index idx exists in the graph and is a live pointer.
virtual	~FactorGraph ()=default
	Default destructor Public and virtual so boost serialization can call it.
template<class DERIVEDFACTOR, typename = IsDerived<DERIVEDFACTOR>>
	FactorGraph (std::initializer_list< boost::shared_ptr< DERIVEDFACTOR > > sharedFactors)
	Constructor that takes an initializer list of shared pointers.
void	reserve (size_t size)
	Reserve space for the specified number of factors if you know in advance how many there will be (works like FastVector::reserve).
template<class DERIVEDFACTOR>
IsDerived< DERIVEDFACTOR >	push_back (boost::shared_ptr< DERIVEDFACTOR > factor)
	Add a factor directly using a shared_ptr.
template<class DERIVEDFACTOR, class... Args>
IsDerived< DERIVEDFACTOR >	emplace_shared (Args &&... args)
	Emplace a shared pointer to factor of given type.
template<class DERIVEDFACTOR>
IsDerived< DERIVEDFACTOR >	push_back (const DERIVEDFACTOR &factor)
	Add a factor by value, will be copy-constructed (use push_back with a shared_ptr to avoid the copy).
template<class DERIVEDFACTOR>
IsDerived< DERIVEDFACTOR >	add (boost::shared_ptr< DERIVEDFACTOR > factor)
	add is a synonym for push_back.
template<class DERIVEDFACTOR>
std::enable_if< std::is_base_of< FactorType, DERIVEDFACTOR >::value, boost::assign::list_inserter< RefCallPushBack< This > > >::type	operator+= (boost::shared_ptr< DERIVEDFACTOR > factor)
	+= works well with boost::assign list inserter.
template<typename ITERATOR>
HasDerivedElementType< ITERATOR >	push_back (ITERATOR firstFactor, ITERATOR lastFactor)
	Push back many factors with an iterator over shared_ptr (factors are not copied).
template<typename ITERATOR>
HasDerivedValueType< ITERATOR >	push_back (ITERATOR firstFactor, ITERATOR lastFactor)
	Push back many factors with an iterator (factors are copied).
template<typename CONTAINER>
HasDerivedElementType< CONTAINER >	push_back (const CONTAINER &container)
	Push back many factors as shared_ptr's in a container (factors are not copied).
template<typename CONTAINER>
HasDerivedValueType< CONTAINER >	push_back (const CONTAINER &container)
	Push back non-pointer objects in a container (factors are copied).
template<class FACTOR_OR_CONTAINER>
void	add (const FACTOR_OR_CONTAINER &factorOrContainer)
	Add a factor or container of factors, including STL collections, BayesTrees, etc.
template<class FACTOR_OR_CONTAINER>
boost::assign::list_inserter< CRefCallPushBack< This > >	operator+= (const FACTOR_OR_CONTAINER &factorOrContainer)
	Add a factor or container of factors, including STL collections, BayesTrees, etc.
template<class CLIQUE>
std::enable_if< std::is_base_of< This, typenameCLIQUE::FactorGraphType >::value >::type	push_back (const BayesTree< CLIQUE > &bayesTree)
	Push back a BayesTree as a collection of factors.
template<typename CONTAINER, typename = HasDerivedElementType<CONTAINER>>
FactorIndices	add_factors (const CONTAINER &factors, bool useEmptySlots=false)
	Add new factors to a factor graph and returns a list of new factor indices, optionally finding and reusing empty factor slots.
bool	equals (const This &fg, double tol=1e-9) const
	Check equality up to tolerance.
size_t	size () const
	return the number of factors (including any null factors set by remove() ).
bool	empty () const
	Check if the graph is empty (null factors set by remove() will cause this to return false).
const sharedFactor	at (size_t i) const
	Get a specific factor by index (this checks array bounds and may throw an exception, as opposed to operator[] which does not).
sharedFactor &	at (size_t i)
	Get a specific factor by index (this checks array bounds and may throw an exception, as opposed to operator[] which does not).
const sharedFactor	operator[] (size_t i) const
	Get a specific factor by index (this does not check array bounds, as opposed to at() which does).
sharedFactor &	operator[] (size_t i)
	Get a specific factor by index (this does not check array bounds, as opposed to at() which does).
const_iterator	begin () const
	Iterator to beginning of factors.
const_iterator	end () const
	Iterator to end of factors.
sharedFactor	front () const
	Get the first factor.
sharedFactor	back () const
	Get the last factor.
double	error (const HybridValues &values) const
	Add error for all factors.
iterator	begin ()
	non-const STL-style begin()
iterator	end ()
	non-const STL-style end()
virtual void	resize (size_t size)
	Directly resize the number of factors in the graph.
void	remove (size_t i)
	delete factor without re-arranging indexes by inserting a nullptr pointer
void	replace (size_t index, sharedFactor factor)
	replace a factor by index
iterator	erase (iterator item)
	Erase factor and rearrange other factors to take up the empty space.
iterator	erase (iterator first, iterator last)
	Erase factors and rearrange other factors to take up the empty space.
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.

Public Types
using	Base = BayesNet<HybridConditional>
using	This = HybridBayesNet
using	ConditionalType = HybridConditional
using	shared_ptr = boost::shared_ptr<HybridBayesNet>
using	sharedConditional = boost::shared_ptr<ConditionalType>
Public Types inherited from gtsam::BayesNet< HybridConditional >
typedef boost::shared_ptr< HybridConditional >	sharedConditional
	A shared pointer to a conditional.
Public Types inherited from gtsam::FactorGraph< FACTOR >
typedef FACTOR	FactorType
	factor type
typedef boost::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

Friends
class	boost::serialization::access
	Serialization function.

Additional Inherited Members
Protected Member Functions inherited from gtsam::BayesNet< HybridConditional >
	BayesNet ()
	Default constructor as an empty BayesNet.
	BayesNet (ITERATOR firstConditional, ITERATOR lastConditional)
	Construct from iterator over conditionals.
	BayesNet (std::initializer_list< sharedConditional > conditionals)
	Constructor that takes an initializer list of shared pointers.
Protected Member Functions inherited from gtsam::FactorGraph< FACTOR >
bool	isEqual (const FactorGraph &other) const
	Check exact equality of the factor pointers. Useful for derived ==.
	FactorGraph ()
	Default constructor.
template<typename ITERATOR>
	FactorGraph (ITERATOR firstFactor, ITERATOR lastFactor)
	Constructor from iterator over factors (shared_ptr or plain objects).
template<class CONTAINER>
	FactorGraph (const CONTAINER &factors)
	Construct from container of factors (shared_ptr or plain objects).
Protected Attributes inherited from gtsam::FactorGraph< FACTOR >
FastVector< sharedFactor >	factors_
	concept check, makes sure FACTOR defines print and equals

Member Function Documentation

◆ choose()

GaussianBayesNet gtsam::HybridBayesNet::choose ( const DiscreteValues & assignment ) const

Get the Gaussian Bayes Net which corresponds to a specific discrete value assignment.

Parameters

assignment The discrete value assignment for the discrete keys.

Returns: GaussianBayesNet

◆ discreteConditionals()

DecisionTreeFactor::shared_ptr gtsam::HybridBayesNet::discreteConditionals ( ) const

Get all the discrete conditionals as a decision tree factor.

Returns: DecisionTreeFactor::shared_ptr

◆ emplace_back()

template<class Conditional>

void gtsam::HybridBayesNet::emplace_back ( Conditional * conditional )

inline

Preferred: add a conditional directly using a pointer.

Examples: hbn.emplace_back(new GaussianMixture(...))); hbn.emplace_back(new GaussianConditional(...))); hbn.emplace_back(new DiscreteConditional(...)));

◆ evaluate()

AlgebraicDecisionTree< Key > gtsam::HybridBayesNet::evaluate ( const VectorValues & continuousValues ) const

Compute unnormalized probability q(μ|M), for each discrete assignment, and return as a tree.

q(μ|M) is the unnormalized probability at the MLE point μ, conditioned on the discrete variables.

Parameters

continuousValues Continuous values at which to compute the probability.

Returns: AlgebraicDecisionTree<Key>

◆ logProbability()

AlgebraicDecisionTree< Key > gtsam::HybridBayesNet::logProbability ( const VectorValues & continuousValues ) const

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

Parameters

continuousValues Continuous values at which to compute the error.

Returns: AlgebraicDecisionTree<Key>

◆ optimize() [1/2]

HybridValues gtsam::HybridBayesNet::optimize ( ) const

Solve the HybridBayesNet by first computing the MPE of all the discrete variables and then optimizing the continuous variables based on the MPE assignment.

Returns: HybridValues

◆ optimize() [2/2]

VectorValues gtsam::HybridBayesNet::optimize ( const DiscreteValues & assignment ) const

Given the discrete assignment, return the optimized estimate for the selected Gaussian BayesNet.

Parameters

assignment An assignment of discrete values.

Returns: Values

◆ print()

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

overridevirtual

GTSAM-style printing.

Reimplemented from gtsam::BayesNet< HybridConditional >.

◆ push_back() [1/2]

void gtsam::HybridBayesNet::push_back ( boost::shared_ptr< HybridConditional > conditional )

inline

Add a hybrid conditional using a shared_ptr.

This is the "native" push back, as this class stores hybrid conditionals.

◆ push_back() [2/2]

void gtsam::HybridBayesNet::push_back ( HybridConditional && conditional )

inline

Add a conditional using a shared_ptr, using implicit conversion to a HybridConditional.

This is useful when you create a conditional shared pointer as you need it somewhere else.

Example: auto shared_ptr_to_a_conditional = boost::make_shared<GaussianMixture>(...); hbn.push_back(shared_ptr_to_a_conditional);

◆ sample() [1/4]

HybridValues gtsam::HybridBayesNet::sample ( ) const

Sample using ancestral sampling, use default rng.

Returns: HybridValues

◆ sample() [2/4]

HybridValues gtsam::HybridBayesNet::sample ( const HybridValues & given ) const

Sample from an incomplete BayesNet, use default rng.

Parameters

given Values of missing variables.

Returns: HybridValues

◆ sample() [3/4]

HybridValues gtsam::HybridBayesNet::sample	(	const HybridValues &	given,
		std::mt19937_64 *	rng ) const

Sample from an incomplete BayesNet, given missing variables.

Example: std::mt19937_64 rng(42); VectorValues given = ...; auto sample = bn.sample(given, &rng);

Parameters

given	Values of missing variables.
rng	The pseudo-random number generator.

Returns: HybridValues

◆ sample() [4/4]

HybridValues gtsam::HybridBayesNet::sample ( std::mt19937_64 * rng ) const

Sample using ancestral sampling.

Example: std::mt19937_64 rng(42); auto sample = bn.sample(&rng);

Parameters

rng	The pseudo-random number generator.

Returns: HybridValues

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

/tmp/gtsam-4.2-docs.H5EUbA/src/gtsam/hybrid/HybridBayesNet.h
/tmp/gtsam-4.2-docs.H5EUbA/src/gtsam/hybrid/HybridBayesNet.cpp

Detailed Description

Public Member Functions

Public Types

Friends

Additional Inherited Members

Member Function Documentation

◆ choose()

◆ discreteConditionals()

◆ emplace_back()

◆ evaluate()

◆ logProbability()

◆ optimize() [1/2]

◆ optimize() [2/2]

◆ print()

◆ push_back() [1/2]

◆ push_back() [2/2]

◆ sample() [1/4]

◆ sample() [2/4]

◆ sample() [3/4]

◆ sample() [4/4]