#include <HybridBayesNet.h>

Inheritance diagram for gtsam::HybridBayesNet:

Collaboration diagram for gtsam::HybridBayesNet:

Public Types
using	Base = BayesNet< HybridConditional >

using	This = HybridBayesNet

using	ConditionalType = HybridConditional

using	shared_ptr = std::shared_ptr< HybridBayesNet >

using	sharedConditional = std::shared_ptr< ConditionalType >

typedef HybridConditional	FactorType
	factor type

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

typedef sharedFactor	value_type

typedef FastVector< sharedFactor >::iterator	iterator

typedef FastVector< sharedFactor >::const_iterator	const_iterator

Public Member Functions
Standard Constructors
	HybridBayesNet ()=default

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 (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.

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. 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.

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

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.

HybridValues methods
double	logProbability (const HybridValues &x) const

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)

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

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
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)

Advanced Interface
size_t	nrFactors () const

KeySet	keys () const

KeyVector	keyVector () const

bool	exists (size_t idx) const

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

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

Constructor & Destructor Documentation

◆ HybridBayesNet()

gtsam::HybridBayesNet::HybridBayesNet ( )

default

Construct empty Bayes net

Member Function Documentation

◆ add()

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

inlineinherited

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

◆ add_factors()

FactorIndices gtsam::FactorGraph< HybridConditional >::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.

◆ at() [1/2]

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

inlineinherited

Get the last factor

◆ begin() [1/2]

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

inlineinherited

Iterator to beginning of factors.

◆ begin() [2/2]

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

inlineinherited

non-const STL-style begin()

◆ 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(...)));

◆ empty()

bool gtsam::FactorGraph< HybridConditional >::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< HybridConditional >::end ( ) const

inlineinherited

Iterator to end of factors.

◆ end() [2/2]

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

inlineinherited

non-const STL-style end()

◆ erase() [1/2]

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

inlineinherited

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

◆ erase() [2/2]

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

inlineinherited

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

◆ error()

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

inherited

Add error for all factors.

◆ 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>

◆ exists()

bool gtsam::FactorGraph< HybridConditional >::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< HybridConditional >::front ( ) const

inlineinherited

Get the first factor

◆ keys()

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

inherited

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

◆ keyVector()

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

inherited

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

◆ 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>

◆ nrFactors()

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

inherited

return the number of non-null factors

◆ operator[]() [1/2]

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

inlineinherited

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

◆ 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

◆ push_back() [1/6]

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.

◆ push_back() [2/6]

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);

◆ push_back() [3/6]

IsDerived<DERIVEDFACTOR> gtsam::FactorGraph< HybridConditional >::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() [4/6]

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

inlineinherited

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

◆ push_back() [5/6]

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

inlineinherited

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

◆ push_back() [6/6]

std::enable_if< std::is_base_of<This, typename CLIQUE::FactorGraphType>::value>::type gtsam::FactorGraph< HybridConditional >::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< HybridConditional >::remove ( size_t i )

inlineinherited

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

◆ replace()

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

inlineinherited

replace a factor by index

◆ reserve()

void gtsam::FactorGraph< HybridConditional >::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< HybridConditional >::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.

◆ 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

◆ 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

◆ 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

◆ sample() [4/4]

HybridValues gtsam::HybridBayesNet::sample ( ) const

Sample using ancestral sampling, use default rng.

Returns: HybridValues

◆ size()

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

inlineinherited

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

◆ 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.

Member Data Documentation

◆ factors_

FastVector<sharedFactor> gtsam::FactorGraph< HybridConditional >::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/HybridBayesNet.h

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ HybridBayesNet()

Member Function Documentation

◆ add()

◆ add_factors()

◆ at() [1/2]

◆ at() [2/2]

◆ back()

◆ begin() [1/2]

◆ begin() [2/2]

◆ choose()

◆ discreteConditionals()

◆ emplace_back()

◆ empty()

◆ end() [1/2]

◆ end() [2/2]

◆ erase() [1/2]

◆ erase() [2/2]

◆ error()

◆ evaluate()

◆ exists()

◆ front()

◆ keys()

◆ keyVector()

◆ logProbability()

◆ nrFactors()

◆ operator[]() [1/2]

◆ operator[]() [2/2]

◆ optimize() [1/2]

◆ optimize() [2/2]

◆ push_back() [1/6]

◆ push_back() [2/6]

◆ push_back() [3/6]

◆ push_back() [4/6]

◆ push_back() [5/6]

◆ push_back() [6/6]

◆ remove()

◆ replace()

◆ reserve()

◆ resize()

◆ sample() [1/4]

◆ sample() [2/4]

◆ sample() [3/4]

◆ sample() [4/4]

◆ size()

◆ toFactorGraph()

Member Data Documentation

◆ factors_