#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

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...

template<class Conditional >
void	emplace_back (Conditional *conditional)

void	push_back (HybridConditional &&conditional)

GaussianBayesNet	choose (const DiscreteValues &assignment) const
	Get the Gaussian Bayes Net which corresponds to a specific discrete value assignment. 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...

DecisionTreeFactor::shared_ptr	discreteConditionals () const
	Get all the discrete conditionals as a decision tree factor. 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)
	Prune the Hybrid Bayes Net such that we have at most maxNrLeaves leaves. More...

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

AlgebraicDecisionTree< Key >	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. More...

HybridGaussianFactorGraph	toFactorGraph (const VectorValues &measurements) 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...

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)

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)

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

Private Member Functions
void	updateDiscreteConditionals (const DecisionTreeFactor &prunedDecisionTree)
	Update the discrete conditionals with the pruned versions. More...

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, Gaussian mixtures, or pure Gaussian conditionals.

Definition at line 35 of file HybridBayesNet.h.

Member Typedef Documentation

◆ Base

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

Definition at line 37 of file HybridBayesNet.h.

◆ ConditionalType

using gtsam::HybridBayesNet::ConditionalType = HybridConditional

Definition at line 39 of file HybridBayesNet.h.

◆ shared_ptr

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

Definition at line 40 of file HybridBayesNet.h.

◆ sharedConditional

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

Definition at line 41 of file HybridBayesNet.h.

◆ This

using gtsam::HybridBayesNet::This = HybridBayesNet

Definition at line 38 of file HybridBayesNet.h.

Constructor & Destructor Documentation

◆ HybridBayesNet()

gtsam::HybridBayesNet::HybridBayesNet ( )

default

Construct empty Bayes net

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

Definition at line 213 of file HybridBayesNet.cpp.

◆ discreteConditionals()

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

Get all the discrete conditionals as a decision tree factor.

Returns: DecisionTreeFactor::shared_ptr

Definition at line 41 of file HybridBayesNet.cpp.

◆ 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(...)));

Definition at line 82 of file HybridBayesNet.h.

◆ equals()

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

GTSAM-style equals.

Definition at line 36 of file HybridBayesNet.cpp.

◆ evaluate() [1/2]

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

Evaluate hybrid probability density for given HybridValues.

Definition at line 336 of file HybridBayesNet.cpp.

◆ evaluate() [2/2]

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>

Definition at line 329 of file HybridBayesNet.cpp.

◆ 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>

Definition at line 297 of file HybridBayesNet.cpp.

◆ operator()()

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

inline

Evaluate hybrid probability density for given HybridValues, sugar.

Definition at line 117 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 233 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 250 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 30 of file HybridBayesNet.cpp.

◆ prune()

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

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

Definition at line 176 of file HybridBayesNet.cpp.

◆ push_back() [1/2]

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 69 of file HybridBayesNet.h.

◆ 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 = std::make_shared<GaussianMixture>(...); hbn.push_back(shared_ptr_to_a_conditional);

Definition at line 99 of file HybridBayesNet.h.

◆ sample() [1/4]

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 262 of file HybridBayesNet.cpp.

◆ sample() [2/4]

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 281 of file HybridBayesNet.cpp.

◆ sample() [3/4]

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 287 of file HybridBayesNet.cpp.

◆ sample() [4/4]

HybridValues gtsam::HybridBayesNet::sample ( ) const

Sample using ancestral sampling, use default rng.

Returns: HybridValues

Definition at line 292 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 341 of file HybridBayesNet.cpp.

◆ updateDiscreteConditionals()

void gtsam::HybridBayesNet::updateDiscreteConditionals ( const DecisionTreeFactor & prunedDecisionTree )

private

Update the discrete conditionals with the pruned versions.

Parameters

prunedDecisionTree

Definition at line 146 of file HybridBayesNet.cpp.

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

Public Types

Public Member Functions

Private Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ Base

◆ ConditionalType

◆ shared_ptr

◆ sharedConditional

◆ This

Constructor & Destructor Documentation

◆ HybridBayesNet()

Member Function Documentation

◆ choose()

◆ discreteConditionals()

◆ emplace_back()

◆ equals()

◆ evaluate() [1/2]

◆ evaluate() [2/2]

◆ logProbability()

◆ operator()()

◆ optimize() [1/2]

◆ optimize() [2/2]

◆ print()

◆ prune()

◆ push_back() [1/2]

◆ push_back() [2/2]

◆ sample() [1/4]

◆ sample() [2/4]

◆ sample() [3/4]

◆ sample() [4/4]

◆ toFactorGraph()

◆ updateDiscreteConditionals()