gtsam::Conditional< FACTOR, DERIVEDCONDITIONAL > Class Template Reference

Advanced Interface
FACTOR::const_iterator	beginFrontals () const
	Iterator pointing to first frontal key.
FACTOR::const_iterator	endFrontals () const
	Iterator pointing past the last frontal key.
FACTOR::const_iterator	beginParents () const
	Iterator pointing to the first parent key.
FACTOR::const_iterator	endParents () const
	Iterator pointing past the last parent key.
size_t &	nrFrontals ()
	Mutable version of nrFrontals.
FACTOR::iterator	beginFrontals ()
	Mutable iterator pointing to first frontal key.
FACTOR::iterator	endFrontals ()
	Mutable iterator pointing past the last frontal key.
FACTOR::iterator	beginParents ()
	Mutable iterator pointing to the first parent key.
FACTOR::iterator	endParents ()
	Mutable iterator pointing past the last parent key.
template<class VALUES>
static bool	CheckInvariants (const DERIVEDCONDITIONAL &conditional, const VALUES &x)
	Check invariants of this conditional, given the values x.

Public Member Functions
Testable
void	print (const std::string &s="Conditional", const KeyFormatter &formatter=DefaultKeyFormatter) const
	print with optional formatter
bool	equals (const This &c, double tol=1e-9) const
	check equality
Standard Interface
size_t	nrFrontals () const
	return the number of frontals
size_t	nrParents () const
	return the number of parents
Key	firstFrontalKey () const
	Convenience function to get the first frontal key.
Frontals	frontals () const
	return a view of the frontal keys
Parents	parents () const
	return a view of the parent keys
virtual double	logProbability (const HybridValues &c) const
	All conditional types need to implement a logProbability function, for which exp(logProbability(x)) = evaluate(x).
virtual double	evaluate (const HybridValues &c) const
	All conditional types need to implement an evaluate function, that yields a true probability.
double	operator() (const HybridValues &x) const
	Evaluate probability density, sugar.
virtual double	negLogConstant () const
	All conditional types need to implement this as the negative log of the normalization constant to make it such that error>=0.

Public Types
typedef std::pair< typename FACTOR::const_iterator, typename FACTOR::const_iterator >	ConstFactorRange
	A mini implementation of an iterator range, to share const views of frontals and parents.
typedef ConstFactorRangeIterator	Frontals
	View of the frontal keys (call frontals()).
typedef ConstFactorRangeIterator	Parents
	View of the separator keys (call parents()).

Classes
struct	ConstFactorRangeIterator

Protected Member Functions
Standard Constructors
	Conditional ()
	Empty Constructor to make serialization possible.
	Conditional (size_t nrFrontals)
	Constructor.

Protected Attributes
size_t	nrFrontals_
	The first nrFrontal variables are frontal and the rest are parents.

Member Function Documentation

CheckInvariants()

template<class FACTOR, class DERIVEDCONDITIONAL>

template<class VALUES>

bool gtsam::Conditional< FACTOR, DERIVEDCONDITIONAL >::CheckInvariants ( const DERIVEDCONDITIONAL & conditional, const VALUES & x )

static

Check invariants of this conditional, given the values x.

It tests:

• evaluate >= 0.0
• evaluate(x) == conditional(x)
• exp(logProbability(x)) == evaluate(x)
• negLogConstant() = -log(normalizationConstant())
• error >= 0.0
• logProbability(x) == -(negLogConstant() + error(x))

Parameters

conditional

The conditional to test, as a reference to the derived type.

Template Parameters

VALUES

HybridValues, or a more narrow type like DiscreteValues.

evaluate()

template<class FACTOR, class DERIVEDCONDITIONAL>

double gtsam::Conditional< FACTOR, DERIVEDCONDITIONAL >::evaluate ( const HybridValues & c ) const

virtual

All conditional types need to implement an evaluate function, that yields a true probability.

The default implementation just exponentiates logProbability.

Reimplemented in gtsam::DiscreteConditional, gtsam::GaussianConditional, gtsam::HybridConditional, gtsam::HybridGaussianConditional, gtsam::SymbolicConditional, and gtsam::TableDistribution.

logProbability()

template<class FACTOR, class DERIVEDCONDITIONAL>

double gtsam::Conditional< FACTOR, DERIVEDCONDITIONAL >::logProbability ( const HybridValues & c ) const

virtual

All conditional types need to implement a logProbability function, for which exp(logProbability(x)) = evaluate(x).

Reimplemented in gtsam::DiscreteConditional, gtsam::GaussianConditional, gtsam::HybridConditional, gtsam::HybridGaussianConditional, and gtsam::SymbolicConditional.

negLogConstant()

template<class FACTOR, class DERIVEDCONDITIONAL>

double gtsam::Conditional< FACTOR, DERIVEDCONDITIONAL >::negLogConstant ( ) const

virtual

All conditional types need to implement this as the negative log of the normalization constant to make it such that error>=0.

Returns

double

Reimplemented in gtsam::DiscreteConditional, gtsam::GaussianConditional, gtsam::HybridConditional, and gtsam::HybridGaussianConditional.

---

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

• /tmp/gtsam-4.3.0-doxygen.rsXPUS/source/gtsam/inference/Conditional.h
• /tmp/gtsam-4.3.0-doxygen.rsXPUS/source/gtsam/inference/Conditional-inst.h