#include <NoiseModel.h>

Inheritance diagram for gtsam::noiseModel::Constrained:

Collaboration diagram for gtsam::noiseModel::Constrained:

Public Types
typedef std::shared_ptr< Constrained >	shared_ptr

Public Member Functions
	Constrained (const Vector &sigmas=Z_1x1)

bool	isConstrained () const override
	true if a constrained noise mode, saves slow/clumsy dynamic casting

bool	constrained (size_t i) const
	Return true if a particular dimension is free or constrained.

const Vector &	mu () const
	Access mu as a vector.

double	squaredMahalanobisDistance (const Vector &v) const override

void	print (const std::string &name) const override

Vector	whiten (const Vector &v) const override
	Calculates error vector with weights applied.

Matrix	Whiten (const Matrix &H) const override

void	WhitenInPlace (Matrix &H) const override

void	WhitenInPlace (Eigen::Block< Matrix > H) const override

Diagonal::shared_ptr	QR (Matrix &Ab) const override

shared_ptr	unit () const

Vector	sigmas () const override
	Calculate standard deviations.

Vector	unwhiten (const Vector &v) const override
	Unwhiten an error vector.

double	sigma (size_t i) const

const Vector &	invsigmas () const

double	invsigma (size_t i) const

const Vector &	precisions () const

double	precision (size_t i) const

Matrix	R () const override

bool	equals (const Base &expected, double tol=1e-9) const override

void	WhitenSystem (std::vector< Matrix > &A, Vector &b) const override

void	WhitenSystem (Matrix &A, Vector &b) const override

void	WhitenSystem (Matrix &A1, Matrix &A2, Vector &b) const override

void	WhitenSystem (Matrix &A1, Matrix &A2, Matrix &A3, Vector &b) const override

virtual Matrix	information () const
	Compute information matrix.

virtual Matrix	covariance () const
	Compute covariance matrix.

virtual bool	isUnit () const
	true if a unit noise model, saves slow/clumsy dynamic casting

size_t	dim () const
	Dimensionality.

virtual double	mahalanobisDistance (const Vector &v) const
	Mahalanobis distance.

virtual double	loss (const double squared_distance) const
	loss function, input is Mahalanobis distance

virtual void	whitenInPlace (Vector &v) const

virtual void	whitenInPlace (Eigen::Block< Vector > &v) const

virtual void	unwhitenInPlace (Vector &v) const

virtual void	unwhitenInPlace (Eigen::Block< Vector > &v) const

virtual Vector	unweightedWhiten (const Vector &v) const

virtual double	weight (const Vector &v) const

Static Public Member Functions
static shared_ptr	MixedSigmas (const Vector &mu, const Vector &sigmas)

static shared_ptr	MixedSigmas (const Vector &sigmas)

static shared_ptr	MixedSigmas (double m, const Vector &sigmas)

static shared_ptr	MixedVariances (const Vector &mu, const Vector &variances)

static shared_ptr	MixedVariances (const Vector &variances)

static shared_ptr	MixedPrecisions (const Vector &mu, const Vector &precisions)

static shared_ptr	MixedPrecisions (const Vector &precisions)

static shared_ptr	All (size_t dim)

static shared_ptr	All (size_t dim, const Vector &mu)

static shared_ptr	All (size_t dim, double mu)

static shared_ptr	Sigmas (const Vector &sigmas, bool smart=true)

static shared_ptr	Variances (const Vector &variances, bool smart=true)

static shared_ptr	Precisions (const Vector &precisions, bool smart=true)

static shared_ptr	SqrtInformation (const Matrix &R, bool smart=true)

static shared_ptr	Information (const Matrix &M, bool smart=true)

static shared_ptr	Covariance (const Matrix &covariance, bool smart=true)

Protected Member Functions
	Constrained (const Vector &mu, const Vector &sigmas)

Protected Attributes
Vector	mu_
	Penalty function weight - needs to be large enough to dominate soft constraints.

Vector	sigmas_

Vector	invsigmas_

Vector	precisions_

std::optional< Matrix >	sqrt_information_

size_t	dim_

Detailed Description

A Constrained constrained model is a specialization of Diagonal which allows some or all of the sigmas to be zero, forcing the error to be zero there. All other Gaussian models are guaranteed to have a non-singular square-root information matrix, but this class is specifically equipped to deal with singular noise models, specifically: whiten will return zero on those components that have zero sigma and zero error, unchanged otherwise.

While a hard constraint may seem to be a case in which there is infinite error, we do not ever produce an error value of infinity to allow for constraints to actually be optimized rather than self-destructing if not initialized correctly.

Constructor & Destructor Documentation

◆ Constrained() [1/2]

gtsam::noiseModel::Constrained::Constrained	(	const Vector &	mu,
		const Vector &	sigmas
	)

protected

Constructor that prevents any inf values from appearing in invsigmas or precisions. Allows for specifying mu.

◆ Constrained() [2/2]

gtsam::noiseModel::Constrained::Constrained ( const Vector & sigmas = Z_1x1 )

protected constructor takes sigmas. prevents any inf values from appearing in invsigmas or precisions. mu set to large default value (1000.0)

Member Function Documentation

◆ All() [1/3]

static shared_ptr gtsam::noiseModel::Constrained::All ( size_t dim )

inlinestatic

Fully constrained variations

◆ All() [2/3]

static shared_ptr gtsam::noiseModel::Constrained::All	(	size_t	dim,
		const Vector &	mu
	)

inlinestatic

Fully constrained variations

◆ All() [3/3]

static shared_ptr gtsam::noiseModel::Constrained::All	(	size_t	dim,
		double	mu
	)

inlinestatic

Fully constrained variations with a mu parameter

◆ Covariance()

static shared_ptr gtsam::noiseModel::Gaussian::Covariance	(	const Matrix &	covariance,
		bool	smart = `true`
	)

staticinherited

A Gaussian noise model created by specifying a covariance matrix.

Parameters

covariance	The square covariance Matrix
smart	check if can be simplified to derived class

◆ Information()

static shared_ptr gtsam::noiseModel::Gaussian::Information	(	const Matrix &	M,
		bool	smart = `true`
	)

staticinherited

A Gaussian noise model created by specifying an information matrix.

Parameters

M	The information matrix
smart	check if can be simplified to derived class

◆ invsigmas()

const Vector& gtsam::noiseModel::Diagonal::invsigmas ( ) const

inlineinherited

Return sqrt precisions

◆ MixedPrecisions()

static shared_ptr gtsam::noiseModel::Constrained::MixedPrecisions	(	const Vector &	mu,
		const Vector &	precisions
	)

static

A diagonal noise model created by specifying a Vector of precisions, some of which might be inf

◆ MixedSigmas() [1/3]

static shared_ptr gtsam::noiseModel::Constrained::MixedSigmas	(	const Vector &	mu,
		const Vector &	sigmas
	)

static

A diagonal noise model created by specifying a Vector of standard devations, some of which might be zero

◆ MixedSigmas() [2/3]

static shared_ptr gtsam::noiseModel::Constrained::MixedSigmas ( const Vector & sigmas )

static

A diagonal noise model created by specifying a Vector of standard devations, some of which might be zero

◆ MixedSigmas() [3/3]

static shared_ptr gtsam::noiseModel::Constrained::MixedSigmas	(	double	m,
		const Vector &	sigmas
	)

static

A diagonal noise model created by specifying a Vector of standard devations, some of which might be zero

◆ MixedVariances()

static shared_ptr gtsam::noiseModel::Constrained::MixedVariances	(	const Vector &	mu,
		const Vector &	variances
	)

static

A diagonal noise model created by specifying a Vector of standard devations, some of which might be zero

◆ Precisions()

static shared_ptr gtsam::noiseModel::Diagonal::Precisions	(	const Vector &	precisions,
		bool	smart = `true`
	)

staticinherited

A diagonal noise model created by specifying a Vector of precisions, i.e. i.e. the diagonal of the information matrix, i.e., weights

◆ precisions()

const Vector& gtsam::noiseModel::Diagonal::precisions ( ) const

inlineinherited

Return precisions

◆ QR()

Diagonal::shared_ptr gtsam::noiseModel::Constrained::QR ( Matrix & Ab ) const

overridevirtual

Apply QR factorization to the system [A b], taking into account constraints Q' * [A b] = [R d] Dimensions: (r*m) * m*(n+1) = r*(n+1), where r = min(m,n). This routine performs an in-place factorization on Ab. Below-diagonal elements are set to zero by this routine.

Parameters

Ab	is the m*(n+1) augmented system matrix [A b]

Returns: diagonal noise model can be all zeros, mixed, or not-constrained

Reimplemented from gtsam::noiseModel::Gaussian.

◆ R()

Matrix gtsam::noiseModel::Diagonal::R ( ) const

inlineoverridevirtualinherited

Return R itself, but note that Whiten(H) is cheaper than R*H

Reimplemented from gtsam::noiseModel::Gaussian.

◆ sigma()

double gtsam::noiseModel::Diagonal::sigma ( size_t i ) const

inlineinherited

Return standard deviations (sqrt of diagonal)

◆ Sigmas()

static shared_ptr gtsam::noiseModel::Diagonal::Sigmas	(	const Vector &	sigmas,
		bool	smart = `true`
	)

staticinherited

A diagonal noise model created by specifying a Vector of sigmas, i.e. standard deviations, the diagonal of the square root covariance matrix.

◆ SqrtInformation()

static shared_ptr gtsam::noiseModel::Gaussian::SqrtInformation	(	const Matrix &	R,
		bool	smart = `true`
	)

staticinherited

A Gaussian noise model created by specifying a square root information matrix.

Parameters

R	The (upper-triangular) square root information matrix
smart	check if can be simplified to derived class

◆ squaredMahalanobisDistance()

double gtsam::noiseModel::Constrained::squaredMahalanobisDistance ( const Vector & v ) const

overridevirtual

The squaredMahalanobisDistance function for a constrained noisemodel, for non-constrained versions, uses sigmas, otherwise uses the penalty function with mu

Reimplemented from gtsam::noiseModel::Base.

◆ unit()

shared_ptr gtsam::noiseModel::Constrained::unit ( ) const

Returns a Unit version of a constrained noisemodel in which constrained sigmas remain constrained and the rest are unit scaled

◆ unweightedWhiten()

virtual Vector gtsam::noiseModel::Base::unweightedWhiten ( const Vector & v ) const

inlinevirtualinherited

Useful function for robust noise models to get the unweighted but whitened error

Reimplemented in gtsam::noiseModel::Robust.

◆ unwhitenInPlace() [1/2]

virtual void gtsam::noiseModel::Base::unwhitenInPlace ( Vector & v ) const

inlinevirtualinherited

in-place unwhiten, override if can be done more efficiently

Reimplemented in gtsam::noiseModel::Unit.

◆ unwhitenInPlace() [2/2]

virtual void gtsam::noiseModel::Base::unwhitenInPlace ( Eigen::Block< Vector > & v ) const

inlinevirtualinherited

in-place unwhiten, override if can be done more efficiently

Reimplemented in gtsam::noiseModel::Unit.

◆ Variances()

static shared_ptr gtsam::noiseModel::Diagonal::Variances	(	const Vector &	variances,
		bool	smart = `true`
	)

staticinherited

A diagonal noise model created by specifying a Vector of variances, i.e. i.e. the diagonal of the covariance matrix.

Parameters

variances	A vector containing the variances of this noise model
smart	check if can be simplified to derived class

◆ weight()

virtual double gtsam::noiseModel::Base::weight ( const Vector & v ) const

inlinevirtualinherited

get the weight from the effective loss function on residual vector v

Reimplemented in gtsam::noiseModel::Robust.

◆ Whiten()

Matrix gtsam::noiseModel::Constrained::Whiten ( const Matrix & H ) const

overridevirtual

Whitening functions will perform partial whitening on rows with a non-zero sigma. Other rows remain untouched.

Reimplemented from gtsam::noiseModel::Diagonal.

◆ whitenInPlace() [1/2]

virtual void gtsam::noiseModel::Base::whitenInPlace ( Vector & v ) const

inlinevirtualinherited

in-place whiten, override if can be done more efficiently

Reimplemented in gtsam::noiseModel::Unit, and gtsam::noiseModel::Isotropic.

◆ whitenInPlace() [2/2]

virtual void gtsam::noiseModel::Base::whitenInPlace ( Eigen::Block< Vector > & v ) const

inlinevirtualinherited

in-place whiten, override if can be done more efficiently

Reimplemented in gtsam::noiseModel::Unit.

◆ WhitenInPlace() [1/2]

void gtsam::noiseModel::Constrained::WhitenInPlace ( Matrix & H ) const

overridevirtual

In-place version

Reimplemented from gtsam::noiseModel::Diagonal.

◆ WhitenInPlace() [2/2]

void gtsam::noiseModel::Constrained::WhitenInPlace ( Eigen::Block< Matrix > H ) const

overridevirtual

In-place version

Reimplemented from gtsam::noiseModel::Diagonal.

◆ WhitenSystem()

void gtsam::noiseModel::Gaussian::WhitenSystem	(	std::vector< Matrix > &	A,
		Vector &	b
	)		const

overridevirtualinherited

Whiten a system, in place as well

Implements gtsam::noiseModel::Base.

Member Data Documentation

◆ sigmas_

Vector gtsam::noiseModel::Diagonal::sigmas_

protectedinherited

Standard deviations (sigmas), their inverse and inverse square (weights/precisions) These are all computed at construction: the idea is to use one shared model where computation is done only once, the common use case in many problems.

◆ sqrt_information_

std::optional<Matrix> gtsam::noiseModel::Gaussian::sqrt_information_

protectedinherited

Matrix square root of information matrix (R)

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

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

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ Constrained() [1/2]

◆ Constrained() [2/2]

Member Function Documentation

◆ All() [1/3]

◆ All() [2/3]

◆ All() [3/3]

◆ Covariance()

◆ Information()

◆ invsigmas()

◆ MixedPrecisions()

◆ MixedSigmas() [1/3]

◆ MixedSigmas() [2/3]

◆ MixedSigmas() [3/3]

◆ MixedVariances()

◆ Precisions()

◆ precisions()

◆ QR()

◆ R()

◆ sigma()

◆ Sigmas()

◆ SqrtInformation()

◆ squaredMahalanobisDistance()

◆ unit()

◆ unweightedWhiten()

◆ unwhitenInPlace() [1/2]

◆ unwhitenInPlace() [2/2]

◆ Variances()

◆ weight()

◆ Whiten()

◆ whitenInPlace() [1/2]

◆ whitenInPlace() [2/2]

◆ WhitenInPlace() [1/2]

◆ WhitenInPlace() [2/2]

◆ WhitenSystem()

Member Data Documentation

◆ sigmas_

◆ sqrt_information_