#include <GaussianConditional.h>

Inheritance diagram for gtsam::GaussianConditional:

[legend]

Public Types
typedef Conditional< BaseFactor, This >	BaseConditional
	Typedef to our conditional base class. More...

typedef JacobianFactor	BaseFactor
	Typedef to our factor base class. More...

typedef std::shared_ptr< This >	shared_ptr
	shared_ptr to this class More...

typedef GaussianConditional	This
	Typedef to this class. More...

Public Types inherited from gtsam::JacobianFactor
typedef VerticalBlockMatrix::Block	ABlock

typedef GaussianFactor	Base
	Typedef to base class. More...

typedef ABlock::ColXpr	BVector

typedef VerticalBlockMatrix::constBlock	constABlock

typedef constABlock::ConstColXpr	constBVector

typedef std::shared_ptr< This >	shared_ptr
	shared_ptr to this class More...

typedef JacobianFactor	This
	Typedef to this class. More...

Public Types inherited from gtsam::GaussianFactor
typedef Factor	Base
	Our base class. More...

typedef std::shared_ptr< This >	shared_ptr
	shared_ptr to this class More...

typedef GaussianFactor	This
	This class. More...

Public Types inherited from gtsam::Factor
typedef KeyVector::const_iterator	const_iterator
	Const iterator over keys. More...

typedef KeyVector::iterator	iterator
	Iterator over keys. More...

Public Types inherited from gtsam::Conditional< JacobianFactor, GaussianConditional >
typedef std::pair< typename JacobianFactor ::const_iterator, typename JacobianFactor ::const_iterator >	ConstFactorRange

typedef ConstFactorRangeIterator	Frontals

typedef ConstFactorRangeIterator	Parents

Public Member Functions
Testable
void	print (const std::string &="GaussianConditional", const KeyFormatter &formatter=DefaultKeyFormatter) const override

bool	equals (const GaussianFactor &cg, double tol=1e-9) const override

Standard Interface
double	logNormalizationConstant () const override

double	logProbability (const VectorValues &x) const

double	evaluate (const VectorValues &x) const

double	operator() (const VectorValues &x) const
	Evaluate probability density, sugar. More...

VectorValues	solve (const VectorValues &parents) const

VectorValues	solveOtherRHS (const VectorValues &parents, const VectorValues &rhs) const

void	solveTransposeInPlace (VectorValues &gy) const

JacobianFactor::shared_ptr	likelihood (const VectorValues &frontalValues) const

JacobianFactor::shared_ptr	likelihood (const Vector &frontal) const

VectorValues	sample (std::mt19937_64 *rng) const

VectorValues	sample (const VectorValues &parentsValues, std::mt19937_64 *rng) const

VectorValues	sample () const
	Sample, use default rng. More...

VectorValues	sample (const VectorValues &parentsValues) const
	Sample with given values, use default rng. More...

Linear algebra.
constABlock	R () const

constABlock	S () const

constABlock	S (const_iterator it) const

const constBVector	d () const

double	determinant () const
	Compute the determinant of the R matrix. More...

double	logDeterminant () const
	Compute the log determinant of the R matrix. More...

HybridValues methods.
double	logProbability (const HybridValues &x) const override

double	evaluate (const HybridValues &x) const override

Public Member Functions inherited from gtsam::JacobianFactor
Matrix	augmentedInformation () const override

Matrix	augmentedJacobian () const override

Matrix	augmentedJacobianUnweighted () const

GaussianFactor::shared_ptr	clone () const override

size_t	cols () const

std::pair< std::shared_ptr< GaussianConditional >, shared_ptr >	eliminate (const Ordering &keys)

bool	equals (const GaussianFactor &lf, double tol=1e-9) const override
	assert equality up to a tolerance More...

double	error (const VectorValues &c) const override

Vector	error_vector (const VectorValues &c) const

const SharedDiagonal &	get_model () const

SharedDiagonal &	get_model ()

constABlock	getA (const_iterator variable) const

constABlock	getA () const

ABlock	getA (iterator variable)

ABlock	getA ()

const constBVector	getb () const

BVector	getb ()

DenseIndex	getDim (const_iterator variable) const override

Vector	gradient (Key key, const VectorValues &x) const override
	Compute the gradient wrt a key at any values. More...

VectorValues	gradientAtZero () const override
	A'*b for Jacobian. More...

void	gradientAtZero (double *d) const override
	A'*b for Jacobian (raw memory version) More...

std::map< Key, Matrix >	hessianBlockDiagonal () const override
	Return the block diagonal of the Hessian for this factor. More...

void	hessianDiagonal (double *d) const override
	Raw memory access version of hessianDiagonal. More...

void	hessianDiagonalAdd (VectorValues &d) const override
	Add the current diagonal to a VectorValues instance. More...

Matrix	information () const override

bool	isConstrained () const

std::pair< Matrix, Vector >	jacobian () const override
	Returns (dense) A,b pair associated with factor, bakes in the weights. More...

	JacobianFactor (const GaussianFactor &gf)

	JacobianFactor (const JacobianFactor &jf)

	JacobianFactor (const HessianFactor &hf)

	JacobianFactor ()

	JacobianFactor (const Vector &b_in)

	JacobianFactor (Key i1, const Matrix &A1, const Vector &b, const SharedDiagonal &model=SharedDiagonal())

	JacobianFactor (Key i1, const Matrix &A1, Key i2, const Matrix &A2, const Vector &b, const SharedDiagonal &model=SharedDiagonal())

	JacobianFactor (Key i1, const Matrix &A1, Key i2, const Matrix &A2, Key i3, const Matrix &A3, const Vector &b, const SharedDiagonal &model=SharedDiagonal())

template<typename TERMS >
	JacobianFactor (const TERMS &terms, const Vector &b, const SharedDiagonal &model=SharedDiagonal())

template<typename KEYS >
	JacobianFactor (const KEYS &keys, const VerticalBlockMatrix &augmentedMatrix, const SharedDiagonal &sigmas=SharedDiagonal())

	JacobianFactor (const GaussianFactorGraph &graph)

	JacobianFactor (const GaussianFactorGraph &graph, const VariableSlots &p_variableSlots)

	JacobianFactor (const GaussianFactorGraph &graph, const Ordering &ordering)

	JacobianFactor (const GaussianFactorGraph &graph, const Ordering &ordering, const VariableSlots &p_variableSlots)

std::pair< Matrix, Vector >	jacobianUnweighted () const
	Returns (dense) A,b pair associated with factor, does not bake in weights. More...

const VerticalBlockMatrix &	matrixObject () const

VerticalBlockMatrix &	matrixObject ()

void	multiplyHessianAdd (double alpha, const VectorValues &x, VectorValues &y) const override

void	multiplyHessianAdd (double alpha, const double x, double y, const std::vector< size_t > &accumulatedDims) const

GaussianFactor::shared_ptr	negate () const override

Vector	operator* (const VectorValues &x) const

void	print (const std::string &s="", const KeyFormatter &formatter=DefaultKeyFormatter) const override
	print with optional string More...

size_t	rows () const

void	setModel (bool anyConstrained, const Vector &sigmas)

std::shared_ptr< GaussianConditional >	splitConditional (size_t nrFrontals)

void	transposeMultiplyAdd (double alpha, const Vector &e, VectorValues &x) const

Vector	unweighted_error (const VectorValues &c) const

void	updateHessian (const KeyVector &keys, SymmetricBlockMatrix *info) const override

JacobianFactor	whiten () const

	~JacobianFactor () override

Public Member Functions inherited from gtsam::GaussianFactor
	GaussianFactor ()

template<typename CONTAINER >
	GaussianFactor (const CONTAINER &keys)

double	error (const HybridValues &c) const override

VectorValues	hessianDiagonal () const
	Return the diagonal of the Hessian for this factor. More...

Public Member Functions inherited from gtsam::Factor
virtual	~Factor ()=default
	Default destructor. More...

bool	empty () const
	Whether the factor is empty (involves zero variables). More...

Key	front () const
	First key. More...

Key	back () const
	Last key. More...

const_iterator	find (Key key) const
	find More...

const KeyVector &	keys () const
	Access the factor's involved variable keys. More...

const_iterator	begin () const

const_iterator	end () const

size_t	size () const

virtual void	printKeys (const std::string &s="Factor", const KeyFormatter &formatter=DefaultKeyFormatter) const
	print only keys More...

bool	equals (const This &other, double tol=1e-9) const
	check equality More...

KeyVector &	keys ()

iterator	begin ()

iterator	end ()

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

bool	equals (const This &c, double tol=1e-9) const

virtual	~Conditional ()

size_t	nrFrontals () const

size_t	nrParents () const

Key	firstFrontalKey () const

Frontals	frontals () const

Parents	parents () const

double	operator() (const HybridValues &x) const
	Evaluate probability density, sugar. More...

double	normalizationConstant () const

size_t &	nrFrontals ()

JacobianFactor ::const_iterator	beginFrontals () const

JacobianFactor ::iterator	beginFrontals ()

JacobianFactor ::const_iterator	endFrontals () const

JacobianFactor ::iterator	endFrontals ()

JacobianFactor ::const_iterator	beginParents () const

JacobianFactor ::iterator	beginParents ()

JacobianFactor ::const_iterator	endParents () const

JacobianFactor ::iterator	endParents ()

Constructors
	GaussianConditional ()

	GaussianConditional (Key key, const Vector &d, const Matrix &R, const SharedDiagonal &sigmas=SharedDiagonal())

	GaussianConditional (Key key, const Vector &d, const Matrix &R, Key parent1, const Matrix &S, const SharedDiagonal &sigmas=SharedDiagonal())

	GaussianConditional (Key key, const Vector &d, const Matrix &R, Key parent1, const Matrix &S, Key parent2, const Matrix &T, const SharedDiagonal &sigmas=SharedDiagonal())

template<typename TERMS >
	GaussianConditional (const TERMS &terms, size_t nrFrontals, const Vector &d, const SharedDiagonal &sigmas=SharedDiagonal())

template<typename KEYS >
	GaussianConditional (const KEYS &keys, size_t nrFrontals, const VerticalBlockMatrix &augmentedMatrix, const SharedDiagonal &sigmas=SharedDiagonal())

static GaussianConditional	FromMeanAndStddev (Key key, const Vector &mu, double sigma)
	Construct from mean `mu` and standard deviation `sigma`. More...

static GaussianConditional	FromMeanAndStddev (Key key, const Matrix &A, Key parent, const Vector &b, double sigma)
	Construct from conditional mean `A1 p1 + b` and standard deviation. More...

static GaussianConditional	FromMeanAndStddev (Key key, const Matrix &A1, Key parent1, const Matrix &A2, Key parent2, const Vector &b, double sigma)

template<typename... Args>
static shared_ptr	sharedMeanAndStddev (Args &&... args)
	Create shared pointer by forwarding arguments to fromMeanAndStddev. More...

template<typename ITERATOR >
static shared_ptr	Combine (ITERATOR firstConditional, ITERATOR lastConditional)

Additional Inherited Members
Static Public Member Functions inherited from gtsam::GaussianFactor
template<typename CONTAINER >
static DenseIndex	Slot (const CONTAINER &keys, Key key)

Static Public Member Functions inherited from gtsam::Conditional< JacobianFactor, GaussianConditional >
static bool	CheckInvariants (const GaussianConditional &conditional, const VALUES &x)

Protected Member Functions inherited from gtsam::JacobianFactor
template<typename TERMS >
void	fillTerms (const TERMS &terms, const Vector &b, const SharedDiagonal &noiseModel)
	Internal function to fill blocks and set dimensions. More...

Protected Member Functions inherited from gtsam::Factor
	Factor ()

template<typename CONTAINER >
	Factor (const CONTAINER &keys)

template<typename ITERATOR >
	Factor (ITERATOR first, ITERATOR last)

Protected Member Functions inherited from gtsam::Conditional< JacobianFactor, GaussianConditional >
	Conditional ()

	Conditional (size_t nrFrontals)

Static Protected Member Functions inherited from gtsam::Factor
template<typename CONTAINER >
static Factor	FromKeys (const CONTAINER &keys)

template<typename ITERATOR >
static Factor	FromIterators (ITERATOR first, ITERATOR last)

Protected Attributes inherited from gtsam::JacobianFactor
VerticalBlockMatrix	Ab_

noiseModel::Diagonal::shared_ptr	model_

Protected Attributes inherited from gtsam::Factor
KeyVector	keys_
	The keys involved in this factor. More...

Protected Attributes inherited from gtsam::Conditional< JacobianFactor, GaussianConditional >
size_t	nrFrontals_

Detailed Description

A GaussianConditional functions as the node in a Bayes network. It has a set of parents y,z, etc. and implements a probability density on x. The negative log-density is given by $\frac{1}{2} |Rx - (d - Sy - Tz - ...)|^2$

This is the base class for all conditional distributions/densities, which are implemented as specialized factors. This class does not store any data other than its keys. Derived classes store data such as matrices and probability tables.

The evaluate method is used to evaluate the factor, and together with logProbability is the main methods that need to be implemented in derived classes. These two methods relate to the error method in the factor by: probability(x) = k exp(-error(x)) where k is a normalization constant making probability(x) == 1.0, and logProbability(x) = K - error(x) i.e., K = log(K). The normalization constant K is assumed to not depend on any argument, only (possibly) on the conditional parameters. This class provides a default logNormalizationConstant() == 0.0.

There are four broad classes of conditionals that derive from Conditional:

Gaussian conditionals, implemented in Gaussian density over a set of continuous variables.
Discrete conditionals, implemented in

Definition at line 38 of file GaussianConditional.h.

Member Typedef Documentation

◆ BaseConditional

typedef Conditional<BaseFactor, This> gtsam::GaussianConditional::BaseConditional

Typedef to our conditional base class.

Definition at line 46 of file GaussianConditional.h.

◆ BaseFactor

typedef JacobianFactor gtsam::GaussianConditional::BaseFactor

Typedef to our factor base class.

Definition at line 45 of file GaussianConditional.h.

◆ shared_ptr

typedef std::shared_ptr<This> gtsam::GaussianConditional::shared_ptr

shared_ptr to this class

Definition at line 44 of file GaussianConditional.h.

◆ This

typedef GaussianConditional gtsam::GaussianConditional::This

Typedef to this class.

Definition at line 43 of file GaussianConditional.h.

Constructor & Destructor Documentation

◆ GaussianConditional() [1/6]

gtsam::GaussianConditional::GaussianConditional ( )

inline

default constructor needed for serialization

Definition at line 52 of file GaussianConditional.h.

◆ GaussianConditional() [2/6]

GaussianConditional::GaussianConditional	(	Key	key,
		const Vector &	d,
		const Matrix &	R,
		const SharedDiagonal &	sigmas = `SharedDiagonal()`
	)

constructor with no parents |Rx-d|

Definition at line 45 of file GaussianConditional.cpp.

◆ GaussianConditional() [3/6]

GaussianConditional::GaussianConditional	(	Key	key,
		const Vector &	d,
		const Matrix &	R,
		Key	parent1,
		const Matrix &	S,
		const SharedDiagonal &	sigmas = `SharedDiagonal()`
	)

constructor with only one parent |Rx+Sy-d|

Definition at line 50 of file GaussianConditional.cpp.

◆ GaussianConditional() [4/6]

GaussianConditional::GaussianConditional	(	Key	key,
		const Vector &	d,
		const Matrix &	R,
		Key	parent1,
		const Matrix &	S,
		Key	parent2,
		const Matrix &	T,
		const SharedDiagonal &	sigmas = `SharedDiagonal()`
	)

constructor with two parents |Rx+Sy+Tz-d|

Definition at line 57 of file GaussianConditional.cpp.

◆ GaussianConditional() [5/6]

template<typename TERMS >

GaussianConditional::GaussianConditional	(	const TERMS &	terms,
		size_t	nrFrontals,
		const Vector &	d,
		const SharedDiagonal &	sigmas = `SharedDiagonal()`
	)

Constructor with arbitrary number of frontals and parents.

Template Parameters

TERMS A container whose value type is std::pair<Key, Matrix>, specifying the collection of keys and matrices making up the conditional.

Definition at line 26 of file GaussianConditional-inl.h.

◆ GaussianConditional() [6/6]

template<typename KEYS >

GaussianConditional::GaussianConditional	(	const KEYS &	keys,
		size_t	nrFrontals,
		const VerticalBlockMatrix &	augmentedMatrix,
		const SharedDiagonal &	sigmas = `SharedDiagonal()`
	)

Constructor with arbitrary number keys, and where the augmented matrix is given all together instead of in block terms. Note that only the active view of the provided augmented matrix is used, and that the matrix data is copied into a newly-allocated matrix in the constructed factor.

Definition at line 32 of file GaussianConditional-inl.h.

Member Function Documentation

◆ Combine()

template<typename ITERATOR >

static shared_ptr gtsam::GaussianConditional::Combine	(	ITERATOR	firstConditional,
		ITERATOR	lastConditional
	)

static

Combine several GaussianConditional into a single dense GC. The conditionals enumerated by first and last must be in increasing order, meaning that the parents of any conditional may not include a conditional coming before it.

Parameters

firstConditional	Iterator to the first conditional to combine, must dereference to a shared_ptr<GaussianConditional>.
lastConditional	Iterator to after the last conditional to combine, must dereference to a shared_ptr<GaussianConditional>.

◆ d()

const constBVector gtsam::GaussianConditional::d ( ) const

inline

Get a view of the r.h.s. vector d

Definition at line 225 of file GaussianConditional.h.

◆ determinant()

double gtsam::GaussianConditional::determinant ( ) const

inline

Compute the determinant of the R matrix.

The determinant is computed in log form using logDeterminant for numerical stability and then exponentiated.

Note, the covariance matrix $\Sigma = (R^T R)^{-1}$ , and hence $\det(\Sigma) = 1 / \det(R^T R) = 1 / determinant()^ 2$ .

Returns: double

Definition at line 238 of file GaussianConditional.h.

◆ equals()

bool GaussianConditional::equals	(	const GaussianFactor &	cg,
		double	tol = `1e-9`
	)		const

overridevirtual

equals function

Implements gtsam::GaussianFactor.

Definition at line 131 of file GaussianConditional.cpp.

◆ evaluate() [1/2]

double GaussianConditional::evaluate ( const VectorValues & x ) const

Calculate probability density for given values x: exp(logProbability(x)) == exp(-GaussianFactor::error(x)) / sqrt((2*pi)^n*det(Sigma)) where x is the vector of values, and Sigma is the covariance matrix.

Definition at line 203 of file GaussianConditional.cpp.

◆ evaluate() [2/2]

double GaussianConditional::evaluate ( const HybridValues & x ) const

overridevirtual

Calculate probability for HybridValues x. Simply dispatches to VectorValues version.

Reimplemented from gtsam::Conditional< JacobianFactor, GaussianConditional >.

Definition at line 207 of file GaussianConditional.cpp.

◆ FromMeanAndStddev() [1/3]

GaussianConditional GaussianConditional::FromMeanAndStddev	(	Key	key,
		const Vector &	mu,
		double	sigma
	)

static

Construct from mean mu and standard deviation sigma.

Definition at line 66 of file GaussianConditional.cpp.

◆ FromMeanAndStddev() [2/3]

GaussianConditional GaussianConditional::FromMeanAndStddev	(	Key	key,
		const Matrix &	A,
		Key	parent,
		const Vector &	b,
		double	sigma
	)

static

Construct from conditional mean A1 p1 + b and standard deviation.

Definition at line 77 of file GaussianConditional.cpp.

◆ FromMeanAndStddev() [3/3]

GaussianConditional GaussianConditional::FromMeanAndStddev	(	Key	key,
		const Matrix &	A1,
		Key	parent1,
		const Matrix &	A2,
		Key	parent2,
		const Vector &	b,
		double	sigma
	)

static

Construct from conditional mean A1 p1 + A2 p2 + b and standard deviation sigma.

Definition at line 88 of file GaussianConditional.cpp.

◆ likelihood() [1/2]

JacobianFactor::shared_ptr GaussianConditional::likelihood ( const VectorValues & frontalValues ) const

Convert to a likelihood factor by providing value before bar.

Definition at line 290 of file GaussianConditional.cpp.

◆ likelihood() [2/2]

JacobianFactor::shared_ptr GaussianConditional::likelihood ( const Vector & frontal ) const

Single variable version of likelihood.

Definition at line 322 of file GaussianConditional.cpp.

◆ logDeterminant()

double GaussianConditional::logDeterminant ( ) const

Compute the log determinant of the R matrix.

For numerical stability, the determinant is computed in log form, so it is a summation rather than a multiplication.

Note, the covariance matrix $\Sigma = (R^T R)^{-1}$ , and hence $\log \det(\Sigma) = - \log \det(R^T R) = - 2 logDeterminant()$ .

Returns: double

Definition at line 171 of file GaussianConditional.cpp.

◆ logNormalizationConstant()

double GaussianConditional::logNormalizationConstant ( ) const

overridevirtual

normalization constant = 1.0 / sqrt((2*pi)^n*det(Sigma)) log = - 0.5 * n*log(2*pi) - 0.5 * log det(Sigma)

Reimplemented from gtsam::Conditional< JacobianFactor, GaussianConditional >.

Definition at line 184 of file GaussianConditional.cpp.

◆ logProbability() [1/2]

double GaussianConditional::logProbability ( const VectorValues & x ) const

Calculate log-probability log(evaluate(x)) for given values x: -error(x) - 0.5 * n*log(2*pi) - 0.5 * log det(Sigma) where x is the vector of values, and Sigma is the covariance matrix. This differs from error as it is log, not negative log, and it includes the normalization constant.

Definition at line 194 of file GaussianConditional.cpp.

◆ logProbability() [2/2]

double GaussianConditional::logProbability ( const HybridValues & x ) const

overridevirtual

Calculate log-probability log(evaluate(x)) for HybridValues x. Simply dispatches to VectorValues version.

Reimplemented from gtsam::Conditional< JacobianFactor, GaussianConditional >.

Definition at line 198 of file GaussianConditional.cpp.

◆ operator()()

double gtsam::GaussianConditional::operator() ( const VectorValues & x ) const

inline

Evaluate probability density, sugar.

Definition at line 156 of file GaussianConditional.h.

◆ print()

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

overridevirtual

print

Implements gtsam::GaussianFactor.

Reimplemented in gtsam::GaussianDensity.

Definition at line 101 of file GaussianConditional.cpp.

◆ R()

constABlock gtsam::GaussianConditional::R ( ) const

inline

Return a view of the upper-triangular R block of the conditional

Definition at line 216 of file GaussianConditional.h.

◆ S() [1/2]

constABlock gtsam::GaussianConditional::S ( ) const

inline

Get a view of the parent blocks.

Definition at line 219 of file GaussianConditional.h.

◆ S() [2/2]

constABlock gtsam::GaussianConditional::S ( const_iterator it ) const

inline

Get a view of the S matrix for the variable pointed to by the given key iterator

Definition at line 222 of file GaussianConditional.h.

◆ sample() [1/4]

VectorValues GaussianConditional::sample ( std::mt19937_64 * rng ) const

Sample from conditional, zero parent version Example: std::mt19937_64 rng(42); auto sample = gbn.sample(&rng);

Definition at line 351 of file GaussianConditional.cpp.

◆ sample() [2/4]

VectorValues GaussianConditional::sample	(	const VectorValues &	parentsValues,
		std::mt19937_64 *	rng
	)		const

Sample from conditional, given missing variables Example: std::mt19937_64 rng(42); VectorValues given = ...; auto sample = gbn.sample(given, &rng);

Definition at line 334 of file GaussianConditional.cpp.

◆ sample() [3/4]

VectorValues GaussianConditional::sample ( ) const

Sample, use default rng.

Definition at line 360 of file GaussianConditional.cpp.

◆ sample() [4/4]

VectorValues GaussianConditional::sample ( const VectorValues & parentsValues ) const

Sample with given values, use default rng.

Definition at line 364 of file GaussianConditional.cpp.

◆ sharedMeanAndStddev()

template<typename... Args>

static shared_ptr gtsam::GaussianConditional::sharedMeanAndStddev ( Args &&... args )

inlinestatic

Create shared pointer by forwarding arguments to fromMeanAndStddev.

Definition at line 103 of file GaussianConditional.h.

◆ solve()

VectorValues GaussianConditional::solve ( const VectorValues & parents ) const

Solves a conditional Gaussian and writes the solution into the entries of x for each frontal variable of the conditional. The parents are assumed to have already been solved in and their values are read from x. This function works for multiple frontal variables.

Given the Gaussian conditional with log likelihood $ |R x_f - (d - S x_s)|^2 $ , where $ f $ are the frontal variables and $ s $ are the separator variables of this conditional, this solve function computes $x_f = R^{-1} (d - S x_s)$ using back-substitution.

Parameters

parents VectorValues containing solved parents .

Definition at line 212 of file GaussianConditional.cpp.

◆ solveOtherRHS()

VectorValues GaussianConditional::solveOtherRHS	(	const VectorValues &	parents,
		const VectorValues &	rhs
	)		const

Definition at line 239 of file GaussianConditional.cpp.

◆ solveTransposeInPlace()

void GaussianConditional::solveTransposeInPlace ( VectorValues & gy ) const

Performs transpose backsubstition in place on values

Definition at line 267 of file GaussianConditional.cpp.

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

Public Types

Public Member Functions

Constructors

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ BaseConditional

◆ BaseFactor

◆ shared_ptr

◆ This

Constructor & Destructor Documentation

◆ GaussianConditional() [1/6]

◆ GaussianConditional() [2/6]

◆ GaussianConditional() [3/6]

◆ GaussianConditional() [4/6]

◆ GaussianConditional() [5/6]

◆ GaussianConditional() [6/6]

Member Function Documentation

◆ Combine()

◆ d()

◆ determinant()

◆ equals()

◆ evaluate() [1/2]

◆ evaluate() [2/2]

◆ FromMeanAndStddev() [1/3]

◆ FromMeanAndStddev() [2/3]

◆ FromMeanAndStddev() [3/3]

◆ likelihood() [1/2]

◆ likelihood() [2/2]

◆ logDeterminant()

◆ logNormalizationConstant()

◆ logProbability() [1/2]

◆ logProbability() [2/2]

◆ operator()()

◆ print()

◆ R()

◆ S() [1/2]

◆ S() [2/2]

◆ sample() [1/4]

◆ sample() [2/4]

◆ sample() [3/4]

◆ sample() [4/4]

◆ sharedMeanAndStddev()

◆ solve()

◆ solveOtherRHS()

◆ solveTransposeInPlace()