#include <HybridBayesNet.h>

Inheritance diagram for gtsam::HybridBayesNet:

Public Types
using	Base = BayesNet< HybridConditional >

using	ConditionalType = HybridConditional

using	shared_ptr = std::shared_ptr< HybridBayesNet >

using	sharedConditional = std::shared_ptr< ConditionalType >

using	This = HybridBayesNet

Public Types inherited from gtsam::BayesNet< HybridConditional >
typedef std::shared_ptr< HybridConditional >	sharedConditional
	A shared pointer to a conditional. More...

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

typedef HybridConditional	FactorType
	factor type More...

typedef FastVector< sharedFactor >::iterator	iterator

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

typedef sharedFactor	value_type

Public Member Functions
Standard Constructors
	HybridBayesNet ()=default
	Construct empty Bayes net. More...

	HybridBayesNet (std::initializer_list< HybridConditional::shared_ptr > conditionals)
	Constructor that takes an initializer list of shared pointers. More...

Testable
void	print (const std::string &s="", const KeyFormatter &formatter=DefaultKeyFormatter) const override
	GTSAM-style printing. More...

bool	equals (const This &fg, double tol=1e-9) const
	GTSAM-style equals. More...

Standard Interface
void	push_back (std::shared_ptr< HybridConditional > conditional)
	Add a hybrid conditional using a shared_ptr. More...

void	push_back (HybridConditional &&conditional)

template<class CONDITIONAL >
void	push_back (const std::shared_ptr< CONDITIONAL > &conditional)
	Add a conditional to the Bayes net. Implicitly convert to a HybridConditional. More...

template<class CONDITIONAL , class... Args>
void	emplace_shared (Args &&...args)

DiscreteBayesNet	discreteMarginal () const
	Get the discrete Bayes Net P(M). As the hybrid Bayes net defines P(X,M) = P(X\|M) P(M), this method returns the marginal distribution on the discrete variables. More...

GaussianBayesNet	choose (const DiscreteValues &assignment) const
	Get the Gaussian Bayes net P(X\|M=m) corresponding to a specific assignment m for the discrete variables M. As the hybrid Bayes net defines P(X,M) = P(X\|M) P(M), this method returns the posterior p(X\|M=m). More...

double	evaluate (const HybridValues &values) const
	Evaluate hybrid probability density for given HybridValues. More...

double	operator() (const HybridValues &values) const
	Evaluate hybrid probability density for given HybridValues, sugar. More...

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. More...

VectorValues	optimize (const DiscreteValues &assignment) const
	Given the discrete assignment, return the optimized estimate for the selected Gaussian BayesNet. More...

HybridValues	sample (const HybridValues &given, std::mt19937_64 *rng) const
	Sample from an incomplete BayesNet, given missing variables. More...

HybridValues	sample (std::mt19937_64 *rng) const
	Sample using ancestral sampling. More...

HybridValues	sample (const HybridValues &given) const
	Sample from an incomplete BayesNet, use default rng. More...

HybridValues	sample () const
	Sample using ancestral sampling, use default rng. More...

HybridBayesNet	prune (size_t maxNrLeaves) const
	Prune the Bayes Net such that we have at most maxNrLeaves leaves. More...

AlgebraicDecisionTree< Key >	errorTree (const VectorValues &continuousValues) const
	Compute the negative log posterior log P'(M\|x) of all assignments up to a constant, returning the result as an algebraic decision tree. More...

AlgebraicDecisionTree< Key >	discretePosterior (const VectorValues &continuousValues) const
	Compute normalized posterior P(M\|X=x) and return as a tree. More...

HybridGaussianFactorGraph	toFactorGraph (const VectorValues &measurements) const

double	logProbability (const HybridValues &x) const

Public Member Functions inherited from gtsam::BayesNet< HybridConditional >
void	print (const std::string &s="BayesNet", const KeyFormatter &formatter=DefaultKeyFormatter) const override

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...

double	logProbability (const HybridValues &x) const

double	evaluate (const HybridValues &c) const

Public Member Functions inherited from gtsam::FactorGraph< HybridConditional >
	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

Additional Inherited Members
Protected Member Functions inherited from gtsam::BayesNet< HybridConditional >
	BayesNet ()

	BayesNet (ITERATOR firstConditional, ITERATOR lastConditional)

	BayesNet (std::initializer_list< sharedConditional > conditionals)

Protected Member Functions inherited from gtsam::FactorGraph< HybridConditional >
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< HybridConditional >
FastVector< sharedFactor >	factors_

Detailed Description

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

Definition at line 37 of file HybridBayesNet.h.

Member Typedef Documentation

◆ Base

using gtsam::HybridBayesNet::Base = BayesNet<HybridConditional>

Definition at line 39 of file HybridBayesNet.h.

◆ ConditionalType

using gtsam::HybridBayesNet::ConditionalType = HybridConditional

Definition at line 41 of file HybridBayesNet.h.

◆ shared_ptr

using gtsam::HybridBayesNet::shared_ptr = std::shared_ptr<HybridBayesNet>

Definition at line 42 of file HybridBayesNet.h.

◆ sharedConditional

using gtsam::HybridBayesNet::sharedConditional = std::shared_ptr<ConditionalType>

Definition at line 43 of file HybridBayesNet.h.

◆ This

using gtsam::HybridBayesNet::This = HybridBayesNet

Definition at line 40 of file HybridBayesNet.h.

Constructor & Destructor Documentation

◆ HybridBayesNet() [1/2]

gtsam::HybridBayesNet::HybridBayesNet ( )

default

Construct empty Bayes net.

◆ HybridBayesNet() [2/2]

gtsam::HybridBayesNet::HybridBayesNet ( std::initializer_list< HybridConditional::shared_ptr > conditionals )

inline

Constructor that takes an initializer list of shared pointers.

Definition at line 52 of file HybridBayesNet.h.

Member Function Documentation

◆ choose()

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

Get the Gaussian Bayes net P(X|M=m) corresponding to a specific assignment m for the discrete variables M. As the hybrid Bayes net defines P(X,M) = P(X|M) P(M), this method returns the posterior p(X|M=m).

Parameters

assignment The discrete value assignment for the discrete keys.

Returns: Gaussian posterior P(X|M=m) as a GaussianBayesNet.

Definition at line 103 of file HybridBayesNet.cpp.

◆ discreteMarginal()

DiscreteBayesNet gtsam::HybridBayesNet::discreteMarginal ( ) const

Get the discrete Bayes Net P(M). As the hybrid Bayes net defines P(X,M) = P(X|M) P(M), this method returns the marginal distribution on the discrete variables.

Returns: discrete marginal as a DiscreteBayesNet.

Definition at line 92 of file HybridBayesNet.cpp.

◆ discretePosterior()

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

Compute normalized posterior P(M|X=x) and return as a tree.

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 P(M|X=x) on.

Returns: AlgebraicDecisionTree<Key>

Definition at line 201 of file HybridBayesNet.cpp.

◆ emplace_shared()

template<class CONDITIONAL , class... Args>

void gtsam::HybridBayesNet::emplace_shared ( Args &&... args )

inline

Preferred: Emplace a conditional directly using arguments.

Examples: hbn.emplace_shared<HybridGaussianConditional>(...))); hbn.emplace_shared<GaussianConditional>(...))); hbn.emplace_shared<DiscreteConditional>(...)));

Definition at line 116 of file HybridBayesNet.h.

◆ equals()

bool gtsam::HybridBayesNet::equals	(	const This &	fg,
		double	tol = `1e-9`
	)		const

GTSAM-style equals.

Definition at line 39 of file HybridBayesNet.cpp.

◆ errorTree()

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

Compute the negative log posterior log P'(M|x) of all assignments up to a constant, returning the result as an algebraic decision tree.

Note: The joint P(X,M) is p(X|M) P(M) Then the posterior on M given X=x is is P(M|x) = p(x|M) P(M) / p(x). Ideally we want log P(M|x) = log p(x|M) + log P(M) - log p(x), but unfortunately log p(x) is expensive, so we compute the log of the unnormalized posterior log P'(M|x) = log p(x|M) + log P(M)

Parameters

continuousValues Continuous values x at which to compute log P'(M|x)

Returns: AlgebraicDecisionTree<Key>

Definition at line 188 of file HybridBayesNet.cpp.

◆ evaluate()

double gtsam::HybridBayesNet::evaluate ( const HybridValues & values ) const

Evaluate hybrid probability density for given HybridValues.

Definition at line 210 of file HybridBayesNet.cpp.

◆ logProbability()

double gtsam::BayesNet< CONDITIONAL >::logProbability

Definition at line 94 of file BayesNet-inst.h.

◆ operator()()

double gtsam::HybridBayesNet::operator() ( const HybridValues & values ) const

inline

Evaluate hybrid probability density for given HybridValues, sugar.

Definition at line 145 of file HybridBayesNet.h.

◆ 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

Definition at line 123 of file HybridBayesNet.cpp.

◆ 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

Definition at line 141 of file HybridBayesNet.cpp.

◆ print()

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

overridevirtual

GTSAM-style printing.

Reimplemented from gtsam::FactorGraph< HybridConditional >.

Definition at line 33 of file HybridBayesNet.cpp.

◆ prune()

HybridBayesNet gtsam::HybridBayesNet::prune ( size_t maxNrLeaves ) const

Prune the Bayes Net such that we have at most maxNrLeaves leaves.

Parameters

maxNrLeaves Continuous values at which to compute the error.

Returns: A pruned HybridBayesNet

Definition at line 48 of file HybridBayesNet.cpp.

◆ push_back() [1/3]

template<class CONDITIONAL >

void gtsam::HybridBayesNet::push_back ( const std::shared_ptr< CONDITIONAL > & conditional )

inline

Add a conditional to the Bayes net. Implicitly convert to a HybridConditional.

E.g. hbn.push_back(std::make_shared<DiscreteConditional>(m, "1/1"));

Template Parameters

CONDITIONAL Type of conditional. This is shared_ptr version.

Parameters

conditional The conditional as a shared pointer.

Definition at line 103 of file HybridBayesNet.h.

◆ push_back() [2/3]

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

inline

Move a HybridConditional into a shared pointer and add.

Example: HybridGaussianConditional conditional(...); hbn.push_back(conditional); // loses the original conditional

Definition at line 87 of file HybridBayesNet.h.

◆ push_back() [3/3]

void gtsam::HybridBayesNet::push_back ( std::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.

Definition at line 76 of file HybridBayesNet.h.

◆ sample() [1/4]

HybridValues gtsam::HybridBayesNet::sample ( ) const

Sample using ancestral sampling, use default rng.

Returns: HybridValues

Definition at line 183 of file HybridBayesNet.cpp.

◆ 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

Definition at line 178 of file HybridBayesNet.cpp.

◆ 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

Definition at line 153 of file HybridBayesNet.cpp.

◆ 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

Definition at line 172 of file HybridBayesNet.cpp.

◆ toFactorGraph()

HybridGaussianFactorGraph gtsam::HybridBayesNet::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.

Definition at line 215 of file HybridBayesNet.cpp.

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

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ Base

◆ ConditionalType

◆ shared_ptr

◆ sharedConditional

◆ This

Constructor & Destructor Documentation

◆ HybridBayesNet() [1/2]

◆ HybridBayesNet() [2/2]

Member Function Documentation

◆ choose()

◆ discreteMarginal()

◆ discretePosterior()

◆ emplace_shared()

◆ equals()

◆ errorTree()

◆ evaluate()

◆ logProbability()

◆ operator()()

◆ optimize() [1/2]

◆ optimize() [2/2]

◆ print()

◆ prune()

◆ push_back() [1/3]

◆ push_back() [2/3]

◆ push_back() [3/3]

◆ sample() [1/4]

◆ sample() [2/4]

◆ sample() [3/4]

◆ sample() [4/4]

◆ toFactorGraph()