#include <GaussianConditional.h>

Inheritance diagram for gtsam::GaussianConditional:

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Collaboration diagram for gtsam::GaussianConditional:

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Public Types
typedef GaussianConditional	This
	Typedef to this class.

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

typedef JacobianFactor	BaseFactor
	Typedef to our factor base class.

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

typedef GaussianFactor	Base
	Typedef to base class.

typedef VerticalBlockMatrix::Block	ABlock

typedef VerticalBlockMatrix::constBlock	constABlock

typedef ABlock::ColXpr	BVector

typedef constABlock::ConstColXpr	constBVector

typedef KeyVector::iterator	iterator
	Iterator over keys.

typedef KeyVector::const_iterator	const_iterator
	Const iterator over keys.

typedef std::pair< typename JacobianFactor ::const_iterator, typename JacobianFactor ::const_iterator >	ConstFactorRange

typedef ConstFactorRangeIterator	Frontals

typedef ConstFactorRangeIterator	Parents

Public Member Functions
GaussianFactor::shared_ptr	clone () const override

Vector	unweighted_error (const VectorValues &c) const

Vector	error_vector (const VectorValues &c) const

double	error (const VectorValues &c) const override

Matrix	augmentedInformation () const override

Matrix	information () const override

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

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

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

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

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

Matrix	augmentedJacobian () const override

Matrix	augmentedJacobianUnweighted () const

const VerticalBlockMatrix &	matrixObject () const

VerticalBlockMatrix &	matrixObject ()

GaussianFactor::shared_ptr	negate () const override

bool	isConstrained () const

DenseIndex	getDim (const_iterator variable) const override

size_t	rows () const

size_t	cols () const

const SharedDiagonal &	get_model () const

SharedDiagonal &	get_model ()

const constBVector	getb () const

BVector	getb ()

constABlock	getA (const_iterator variable) const

constABlock	getA () const

ABlock	getA (iterator variable)

ABlock	getA ()

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

Vector	operator* (const VectorValues &x) const

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

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

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

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

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

JacobianFactor	whiten () const

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

void	setModel (bool anyConstrained, const Vector &sigmas)

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

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.

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.

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

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

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

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

Standard Interface
double	error (const HybridValues &c) const override

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

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

Key	front () const
	First key.

Key	back () const
	Last key.

const_iterator	find (Key key) const
	find

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

const_iterator	begin () const

const_iterator	end () const

size_t	size () const

Advanced Interface
KeyVector &	keys ()

iterator	begin ()

iterator	end ()

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

Standard Interface
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.

double	normalizationConstant () const

Static Public Member Functions
Advanced Interface
template<typename CONTAINER >
static DenseIndex	Slot (const CONTAINER &keys, Key key)

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

Static Protected Member Functions
Standard Constructors
template<typename CONTAINER >
static Factor	FromKeys (const CONTAINER &keys)

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

Protected Attributes
VerticalBlockMatrix	Ab_

noiseModel::Diagonal::shared_ptr	model_

KeyVector	keys_
	The keys involved in this factor.

size_t	nrFrontals_

Advanced Interface
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 ()

static bool	CheckInvariants (const GaussianConditional &conditional, const VALUES &x)

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`.

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.

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.

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

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

Member Typedef Documentation

◆ ConstFactorRange

typedef std::pair<typename JacobianFactor ::const_iterator, typename JacobianFactor ::const_iterator> gtsam::Conditional< JacobianFactor , GaussianConditional >::ConstFactorRange

inherited

A mini implementation of an iterator range, to share const views of frontals and parents.

◆ Frontals

typedef ConstFactorRangeIterator gtsam::Conditional< JacobianFactor , GaussianConditional >::Frontals

inherited

View of the frontal keys (call frontals())

◆ Parents

typedef ConstFactorRangeIterator gtsam::Conditional< JacobianFactor , GaussianConditional >::Parents

inherited

View of the separator keys (call parents())

Constructor & Destructor Documentation

◆ GaussianConditional() [1/6]

gtsam::GaussianConditional::GaussianConditional ( )

inline

default constructor needed for serialization

◆ GaussianConditional() [2/6]

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

constructor with no parents |Rx-d|

◆ GaussianConditional() [3/6]

gtsam::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|

◆ GaussianConditional() [4/6]

gtsam::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|

◆ 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.

◆ 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.

Member Function Documentation

◆ augmentedInformation()

Matrix gtsam::JacobianFactor::augmentedInformation ( ) const

overridevirtualinherited

Return the augmented information matrix represented by this GaussianFactor. The augmented information matrix contains the information matrix with an additional column holding the information vector, and an additional row holding the transpose of the information vector. The lower-right entry contains the constant error term (when \( \delta x = 0 \)). The augmented information matrix is described in more detail in HessianFactor, which in fact stores an augmented information matrix.

Implements gtsam::GaussianFactor.

◆ augmentedJacobian()

Matrix gtsam::JacobianFactor::augmentedJacobian ( ) const

overridevirtualinherited

Return (dense) matrix associated with factor. The returned system is an augmented matrix: [A b] weights are baked in

Implements gtsam::GaussianFactor.

◆ augmentedJacobianUnweighted()

Matrix gtsam::JacobianFactor::augmentedJacobianUnweighted ( ) const

inherited

Return (dense) matrix associated with factor. The returned system is an augmented matrix: [A b] weights are not baked in

◆ begin() [1/2]

const_iterator gtsam::Factor::begin ( ) const

inlineinherited

Iterator at beginning of involved variable keys

◆ begin() [2/2]

iterator gtsam::Factor::begin ( )

inlineinherited

Iterator at beginning of involved variable keys

◆ beginFrontals() [1/2]

JacobianFactor ::const_iterator gtsam::Conditional< JacobianFactor , GaussianConditional >::beginFrontals ( ) const

inlineinherited

Iterator pointing to first frontal key.

◆ beginFrontals() [2/2]

JacobianFactor ::iterator gtsam::Conditional< JacobianFactor , GaussianConditional >::beginFrontals ( )

inlineinherited

Mutable iterator pointing to first frontal key.

◆ beginParents() [1/2]

JacobianFactor ::const_iterator gtsam::Conditional< JacobianFactor , GaussianConditional >::beginParents ( ) const

inlineinherited

Iterator pointing to the first parent key.

◆ beginParents() [2/2]

JacobianFactor ::iterator gtsam::Conditional< JacobianFactor , GaussianConditional >::beginParents ( )

inlineinherited

Mutable iterator pointing to the first parent key.

◆ CheckInvariants()

bool gtsam::Conditional< JacobianFactor , GaussianConditional >::CheckInvariants	(	const GaussianConditional< JacobianFactor, GaussianConditional > &	conditional,
		const VALUES &	x
	)

staticinherited

Check invariants of this conditional, given the values x. It tests:

evaluate >= 0.0
evaluate(x) == conditional(x)
exp(logProbability(x)) == evaluate(x)
logNormalizationConstant() = log(normalizationConstant())
error >= 0.0
logProbability(x) == logNormalizationConstant() - 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.

◆ clone()

GaussianFactor::shared_ptr gtsam::JacobianFactor::clone ( ) const

inlineoverridevirtualinherited

Clone this JacobianFactor

Implements gtsam::GaussianFactor.

Reimplemented in gtsam::LinearInequality, gtsam::LinearEquality, and gtsam::LinearCost.

◆ cols()

size_t gtsam::JacobianFactor::cols ( ) const

inlineinherited

return the number of columns in the corresponding linear system

◆ 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

◆ 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

◆ eliminate()

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

inherited

Eliminate the requested variables.

◆ end() [1/2]

const_iterator gtsam::Factor::end ( ) const

inlineinherited

Iterator at end of involved variable keys

◆ end() [2/2]

iterator gtsam::Factor::end ( )

inlineinherited

Iterator at end of involved variable keys

◆ endFrontals() [1/2]

JacobianFactor ::const_iterator gtsam::Conditional< JacobianFactor , GaussianConditional >::endFrontals ( ) const

inlineinherited

Iterator pointing past the last frontal key.

◆ endFrontals() [2/2]

JacobianFactor ::iterator gtsam::Conditional< JacobianFactor , GaussianConditional >::endFrontals ( )

inlineinherited

Mutable iterator pointing past the last frontal key.

◆ endParents() [1/2]

JacobianFactor ::const_iterator gtsam::Conditional< JacobianFactor , GaussianConditional >::endParents ( ) const

inlineinherited

Iterator pointing past the last parent key.

◆ endParents() [2/2]

JacobianFactor ::iterator gtsam::Conditional< JacobianFactor , GaussianConditional >::endParents ( )

inlineinherited

Mutable iterator pointing past the last parent key.

◆ equals() [1/2]

bool gtsam::Conditional< JacobianFactor , GaussianConditional >::equals	(	const This &	c,
		double	tol = `1e-9`
	)		const

inherited

check equality

◆ equals() [2/2]

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

overridevirtual

equals function

Implements gtsam::GaussianFactor.

◆ error()

double gtsam::GaussianFactor::error ( const HybridValues & c ) const

overridevirtualinherited

All factor types need to implement an error function. In factor graphs, this is the negative log-likelihood.

Reimplemented from gtsam::Factor.

◆ error_vector()

Vector gtsam::JacobianFactor::error_vector ( const VectorValues & c ) const

inherited

(A*x-b)

◆ evaluate() [1/2]

double gtsam::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.

◆ evaluate() [2/2]

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

overridevirtual

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

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

◆ firstFrontalKey()

Key gtsam::Conditional< JacobianFactor , GaussianConditional >::firstFrontalKey ( ) const

inlineinherited

Convenience function to get the first frontal key

◆ FromIterators()

template<typename ITERATOR >

static Factor gtsam::Factor::FromIterators	(	ITERATOR	first,
		ITERATOR	last
	)

inlinestaticprotectedinherited

Construct factor from iterator keys. This is called internally from derived factor static factor methods, as a workaround for not being able to call the protected constructors above.

◆ FromKeys()

template<typename CONTAINER >

static Factor gtsam::Factor::FromKeys ( const CONTAINER & keys )

inlinestaticprotectedinherited

Construct factor from container of keys. This is called internally from derived factor static factor methods, as a workaround for not being able to call the protected constructors above.

◆ FromMeanAndStddev()

static GaussianConditional gtsam::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.

◆ frontals()

Frontals gtsam::Conditional< JacobianFactor , GaussianConditional >::frontals ( ) const

inlineinherited

return a view of the frontal keys

◆ get_model() [1/2]

const SharedDiagonal& gtsam::JacobianFactor::get_model ( ) const

inlineinherited

get a copy of model

◆ get_model() [2/2]

SharedDiagonal& gtsam::JacobianFactor::get_model ( )

inlineinherited

get a copy of model (non-const version)

◆ getA() [1/4]

constABlock gtsam::JacobianFactor::getA ( const_iterator variable ) const

inlineinherited

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

◆ getA() [2/4]

constABlock gtsam::JacobianFactor::getA ( ) const

inlineinherited

Get a view of the A matrix, not weighted by noise

◆ getA() [3/4]

ABlock gtsam::JacobianFactor::getA ( iterator variable )

inlineinherited

Get a view of the A matrix for the variable pointed to by the given key iterator (non-const version)

◆ getA() [4/4]

ABlock gtsam::JacobianFactor::getA ( )

inlineinherited

Get a view of the A matrix

◆ getb() [1/2]

const constBVector gtsam::JacobianFactor::getb ( ) const

inlineinherited

Get a view of the r.h.s. vector b, not weighted by noise

◆ getb() [2/2]

BVector gtsam::JacobianFactor::getb ( )

inlineinherited

Get a view of the r.h.s. vector b (non-const version)

◆ getDim()

DenseIndex gtsam::JacobianFactor::getDim ( const_iterator variable ) const

inlineoverridevirtualinherited

Return the dimension of the variable pointed to by the given key iterator todo: Remove this in favor of keeping track of dimensions with variables?

Implements gtsam::GaussianFactor.

◆ information()

Matrix gtsam::JacobianFactor::information ( ) const

overridevirtualinherited

Return the non-augmented information matrix represented by this GaussianFactor.

Implements gtsam::GaussianFactor.

◆ isConstrained()

bool gtsam::JacobianFactor::isConstrained ( ) const

inlineinherited

is noise model constrained ?

◆ keys()

KeyVector& gtsam::Factor::keys ( )

inlineinherited

Returns: keys involved in this factor

◆ likelihood() [1/2]

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

Convert to a likelihood factor by providing value before bar.

◆ likelihood() [2/2]

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

Single variable version of likelihood.

◆ logDeterminant()

double gtsam::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

◆ logNormalizationConstant()

double gtsam::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 >.

◆ logProbability() [1/2]

double gtsam::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.

◆ logProbability() [2/2]

double gtsam::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 >.

◆ matrixObject() [1/2]

const VerticalBlockMatrix& gtsam::JacobianFactor::matrixObject ( ) const

inlineinherited

Return the full augmented Jacobian matrix of this factor as a VerticalBlockMatrix object.

◆ matrixObject() [2/2]

VerticalBlockMatrix& gtsam::JacobianFactor::matrixObject ( )

inlineinherited

Mutable access to the full augmented Jacobian matrix of this factor as a VerticalBlockMatrix object.

◆ multiplyHessianAdd() [1/2]

void gtsam::JacobianFactor::multiplyHessianAdd	(	double	alpha,
		const VectorValues &	x,
		VectorValues &	y
	)		const

overridevirtualinherited

y += alpha * A'*A*x

Implements gtsam::GaussianFactor.

Reimplemented in gtsam::RegularJacobianFactor< D >.

◆ multiplyHessianAdd() [2/2]

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

inherited

Raw memory access version of multiplyHessianAdd y += alpha * A'*A*x Requires the vector accumulatedDims to tell the dimension of each variable: e.g.: x0 has dim 3, x2 has dim 6, x3 has dim 2, then accumulatedDims is [0 3 9 11 13] NOTE: size of accumulatedDims is size of keys + 1!! TODO(frank): we should probably kill this if no longer needed

◆ negate()

GaussianFactor::shared_ptr gtsam::JacobianFactor::negate ( ) const

overridevirtualinherited

Construct the corresponding anti-factor to negate information stored stored in this factor.

Returns: a HessianFactor with negated Hessian matrices

Implements gtsam::GaussianFactor.

◆ normalizationConstant()

double gtsam::Conditional< JacobianFactor , GaussianConditional >::normalizationConstant ( ) const

inherited

Non-virtual, exponentiate logNormalizationConstant.

◆ nrFrontals() [1/2]

size_t gtsam::Conditional< JacobianFactor , GaussianConditional >::nrFrontals ( ) const

inlineinherited

return the number of frontals

◆ nrFrontals() [2/2]

size_t& gtsam::Conditional< JacobianFactor , GaussianConditional >::nrFrontals ( )

inlineinherited

Mutable version of nrFrontals

◆ nrParents()

size_t gtsam::Conditional< JacobianFactor , GaussianConditional >::nrParents ( ) const

inlineinherited

return the number of parents

◆ operator*()

Vector gtsam::JacobianFactor::operator* ( const VectorValues & x ) const

inherited

Return A*x

◆ parents()

Parents gtsam::Conditional< JacobianFactor , GaussianConditional >::parents ( ) const

inlineinherited

return a view of the parent keys

◆ print()

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

overridevirtual

print

Implements gtsam::GaussianFactor.

Reimplemented in gtsam::GaussianDensity.

◆ R()

constABlock gtsam::GaussianConditional::R ( ) const

inline

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

◆ rows()

size_t gtsam::JacobianFactor::rows ( ) const

inlineinherited

return the number of rows in the corresponding linear system

◆ S() [1/2]

constABlock gtsam::GaussianConditional::S ( ) const

inline

Get a view of the parent blocks.

◆ 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

◆ sample() [1/2]

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

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

◆ sample() [2/2]

VectorValues gtsam::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);

◆ setModel()

void gtsam::JacobianFactor::setModel	(	bool	anyConstrained,
		const Vector &	sigmas
	)

inherited

set noiseModel correctly

◆ size()

size_t gtsam::Factor::size ( ) const

inlineinherited

Returns: the number of variables involved in this factor

◆ solve()

VectorValues gtsam::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 \( x_s \).

◆ solveTransposeInPlace()

void gtsam::GaussianConditional::solveTransposeInPlace ( VectorValues & gy ) const

Performs transpose backsubstition in place on values

◆ splitConditional()

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

inherited

splits a pre-factorized factor into a conditional, and changes the current factor to be the remaining component. Performs same operation as eliminate(), but without running QR. NOTE: looks at dimension of noise model to determine how many rows to keep.

Parameters

nrFrontals number of keys to eliminate

◆ transposeMultiplyAdd()

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

inherited

x += alpha * A'*e. If x is initially missing any values, they are created and assumed to start as zero vectors.

◆ updateHessian()

void gtsam::JacobianFactor::updateHessian	(	const KeyVector &	keys,
		SymmetricBlockMatrix *	info
	)		const

overridevirtualinherited

Update an information matrix by adding the information corresponding to this factor (used internally during elimination).

Parameters

scatter	A mapping from variable index to slot index in this HessianFactor
info	The information matrix to be updated

Implements gtsam::GaussianFactor.

◆ whiten()

JacobianFactor gtsam::JacobianFactor::whiten ( ) const

inherited

Return a whitened version of the factor, i.e. with unit diagonal noise model.

Member Data Documentation

◆ nrFrontals_

size_t gtsam::Conditional< JacobianFactor , GaussianConditional >::nrFrontals_

protectedinherited

The first nrFrontal variables are frontal and the rest are parents.

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

/home/docs/checkouts/readthedocs.org/user_builds/gtsam-jlblanco-docs/checkouts/latest/gtsam/linear/GaussianConditional.h
/home/docs/checkouts/readthedocs.org/user_builds/gtsam-jlblanco-docs/checkouts/latest/gtsam/linear/GaussianConditional-inl.h

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Advanced Interface

Constructors

Detailed Description

Member Typedef Documentation

◆ ConstFactorRange

◆ Frontals

◆ Parents

Constructor & Destructor Documentation

◆ GaussianConditional() [1/6]

◆ GaussianConditional() [2/6]

◆ GaussianConditional() [3/6]

◆ GaussianConditional() [4/6]

◆ GaussianConditional() [5/6]

◆ GaussianConditional() [6/6]

Member Function Documentation

◆ augmentedInformation()

◆ augmentedJacobian()

◆ augmentedJacobianUnweighted()

◆ begin() [1/2]

◆ begin() [2/2]

◆ beginFrontals() [1/2]

◆ beginFrontals() [2/2]

◆ beginParents() [1/2]

◆ beginParents() [2/2]

◆ CheckInvariants()

◆ clone()

◆ cols()

◆ Combine()

◆ d()

◆ determinant()

◆ eliminate()

◆ end() [1/2]

◆ end() [2/2]

◆ endFrontals() [1/2]

◆ endFrontals() [2/2]

◆ endParents() [1/2]

◆ endParents() [2/2]

◆ equals() [1/2]

◆ equals() [2/2]

◆ error()

◆ error_vector()

◆ evaluate() [1/2]

◆ evaluate() [2/2]

◆ firstFrontalKey()

◆ FromIterators()

◆ FromKeys()

◆ FromMeanAndStddev()

◆ frontals()

◆ get_model() [1/2]

◆ get_model() [2/2]

◆ getA() [1/4]

◆ getA() [2/4]

◆ getA() [3/4]

◆ getA() [4/4]

◆ getb() [1/2]

◆ getb() [2/2]

◆ getDim()

◆ information()

◆ isConstrained()

◆ keys()

◆ likelihood() [1/2]

◆ likelihood() [2/2]

◆ logDeterminant()

◆ logNormalizationConstant()

◆ logProbability() [1/2]

◆ logProbability() [2/2]

◆ matrixObject() [1/2]

◆ matrixObject() [2/2]

◆ multiplyHessianAdd() [1/2]

◆ multiplyHessianAdd() [2/2]

◆ negate()

◆ normalizationConstant()

◆ nrFrontals() [1/2]

◆ nrFrontals() [2/2]