#include <NoiseModel.h>

Inheritance diagram for gtsam::noiseModel::Gaussian:

[legend]

Public Types
typedef std::shared_ptr< Gaussian >	shared_ptr

Public Types inherited from gtsam::noiseModel::Base
typedef std::shared_ptr< Base >	shared_ptr

Public Member Functions
virtual Matrix	covariance () const
	Compute covariance matrix. More...

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

	Gaussian (size_t dim=1, const std::optional< Matrix > &sqrt_information={})

virtual Matrix	information () const
	Compute information matrix. More...

double	logDeterminant () const
	Compute the log of \|Σ\|. More...

double	negLogConstant () const
	Compute the negative log of the normalization constant for a Gaussian noise model k = 1/\sqrt(\|2πΣ\|). More...

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

virtual std::shared_ptr< Diagonal >	QR (Matrix &Ab) const

virtual Matrix	R () const
	Return R itself, but note that Whiten(H) is cheaper than R*H. More...

Vector	sigmas () const override
	Calculate standard deviations. More...

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

Matrix	Whiten (const Matrix &H) const override

Vector	whiten (const Vector &v) const override
	Whiten an error vector. More...

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

virtual void	WhitenInPlace (Matrix &H) const

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

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

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

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

	~Gaussian () override

Public Member Functions inherited from gtsam::noiseModel::Base
	Base (size_t dim=1)
	primary constructor More...

size_t	dim () const
	Dimensionality. More...

virtual bool	isConstrained () const
	true if a constrained noise model, saves slow/clumsy dynamic casting More...

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

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

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

virtual double	squaredMahalanobisDistance (const Vector &v) const
	Squared Mahalanobis distance v'R'Rv = <Rv,R*v> More...

virtual Vector	unweightedWhiten (const Vector &v) const

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

virtual void	unwhitenInPlace (Vector &v) const

virtual double	weight (const Vector &v) const

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

virtual void	whitenInPlace (Vector &v) const

virtual	~Base ()

Static Public Member Functions
static shared_ptr	Covariance (const Matrix &covariance, bool smart=true)

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

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

Protected Attributes
std::optional< Matrix >	sqrt_information_

Protected Attributes inherited from gtsam::noiseModel::Base
size_t	dim_

Private Member Functions
virtual double	logDetR () const
	Compute the log of \|R\|. Used for computing log(\|Σ\|) More...

const Matrix &	thisR () const

Detailed Description

Gaussian implements the mathematical model |R*x|^2 = |y|^2 with R'*R=inv(Sigma) where y = whiten(x) = R*x x = unwhiten(x) = inv(R)*y as indeed |y|^2 = y'*y = x'*R'*R*x Various derived classes are available that are more efficient. The named constructors return a shared_ptr because, when the smart flag is true, the underlying object might be a derived class such as Diagonal.

Definition at line 168 of file NoiseModel.h.

Member Typedef Documentation

◆ shared_ptr

typedef std::shared_ptr<Gaussian> gtsam::noiseModel::Gaussian::shared_ptr

Definition at line 191 of file NoiseModel.h.

Constructor & Destructor Documentation

◆ Gaussian()

gtsam::noiseModel::Gaussian::Gaussian	(	size_t	dim = `1`,
		const std::optional< Matrix > &	sqrt_information = `{}`
	)

inline

constructor takes square root information matrix

Definition at line 194 of file NoiseModel.h.

◆ ~Gaussian()

gtsam::noiseModel::Gaussian::~Gaussian ( )

inlineoverride

Definition at line 198 of file NoiseModel.h.

Member Function Documentation

◆ covariance()

Matrix gtsam::noiseModel::Gaussian::covariance ( ) const

virtual

Compute covariance matrix.

Definition at line 148 of file NoiseModel.cpp.

◆ Covariance()

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

static

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

Definition at line 115 of file NoiseModel.cpp.

◆ equals()

bool gtsam::noiseModel::Gaussian::equals	(	const Base &	expected,
		double	tol = `1e-9`
	)		const

overridevirtual

Implements gtsam::noiseModel::Base.

Definition at line 140 of file NoiseModel.cpp.

◆ information()

Matrix gtsam::noiseModel::Gaussian::information ( ) const

virtual

Compute information matrix.

Definition at line 240 of file NoiseModel.cpp.

◆ Information()

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

static

A Gaussian noise model created by specifying an information matrix.

Parameters

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

Definition at line 98 of file NoiseModel.cpp.

◆ logDeterminant()

double gtsam::noiseModel::Gaussian::logDeterminant ( ) const

Compute the log of |Σ|.

Definition at line 250 of file NoiseModel.cpp.

◆ logDetR()

double gtsam::noiseModel::Gaussian::logDetR ( ) const

privatevirtual

Compute the log of |R|. Used for computing log(|Σ|)

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

Definition at line 243 of file NoiseModel.cpp.

◆ negLogConstant()

double gtsam::noiseModel::Gaussian::negLogConstant ( ) const

Compute the negative log of the normalization constant for a Gaussian noise model k = 1/\sqrt(|2πΣ|).

Returns: double

Definition at line 259 of file NoiseModel.cpp.

◆ print()

void gtsam::noiseModel::Gaussian::print ( const std::string & name ) const

overridevirtual

Implements gtsam::noiseModel::Base.

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

Definition at line 135 of file NoiseModel.cpp.

◆ QR()

SharedDiagonal gtsam::noiseModel::Gaussian::QR ( Matrix & Ab ) const

virtual

Apply appropriately weighted QR factorization to the system [A b] 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: Empty SharedDiagonal() noise model: R,d are whitened

Reimplemented in gtsam::noiseModel::Constrained.

Definition at line 190 of file NoiseModel.cpp.

◆ R()

virtual Matrix gtsam::noiseModel::Gaussian::R ( ) const

inlinevirtual

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

Reimplemented in gtsam::noiseModel::Diagonal.

Definition at line 263 of file NoiseModel.h.

◆ sigmas()

Vector gtsam::noiseModel::Gaussian::sigmas ( ) const

overridevirtual

Calculate standard deviations.

Reimplemented from gtsam::noiseModel::Base.

Reimplemented in gtsam::noiseModel::Diagonal.

Definition at line 159 of file NoiseModel.cpp.

◆ SqrtInformation()

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

static

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

Definition at line 84 of file NoiseModel.cpp.

◆ thisR()

const Matrix& gtsam::noiseModel::Gaussian::thisR ( ) const

inlineprivate

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

Definition at line 180 of file NoiseModel.h.

◆ unwhiten()

Vector gtsam::noiseModel::Gaussian::unwhiten ( const Vector & v ) const

overridevirtual

Unwhiten an error vector.

Implements gtsam::noiseModel::Base.

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

Definition at line 169 of file NoiseModel.cpp.

◆ Whiten()

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

overridevirtual

Multiply a derivative with R (derivative of whiten) Equivalent to whitening each column of the input matrix.

Implements gtsam::noiseModel::Base.

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

Definition at line 174 of file NoiseModel.cpp.

◆ whiten()

Vector gtsam::noiseModel::Gaussian::whiten ( const Vector & v ) const

overridevirtual

Whiten an error vector.

Implements gtsam::noiseModel::Base.

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

Definition at line 164 of file NoiseModel.cpp.

◆ WhitenInPlace() [1/2]

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

virtual

In-place version

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

Definition at line 184 of file NoiseModel.cpp.

◆ WhitenInPlace() [2/2]

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

virtual

In-place version

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

Definition at line 179 of file NoiseModel.cpp.

◆ WhitenSystem() [1/4]

void gtsam::noiseModel::Gaussian::WhitenSystem	(	Matrix &	A,
		Vector &	b
	)		const

overridevirtual

Implements gtsam::noiseModel::Base.

Definition at line 222 of file NoiseModel.cpp.

◆ WhitenSystem() [2/4]

void gtsam::noiseModel::Gaussian::WhitenSystem	(	Matrix &	A1,
		Matrix &	A2,
		Matrix &	A3,
		Vector &	b
	)		const

overridevirtual

Implements gtsam::noiseModel::Base.

Definition at line 233 of file NoiseModel.cpp.

◆ WhitenSystem() [3/4]

void gtsam::noiseModel::Gaussian::WhitenSystem	(	Matrix &	A1,
		Matrix &	A2,
		Vector &	b
	)		const

overridevirtual

Implements gtsam::noiseModel::Base.

Definition at line 227 of file NoiseModel.cpp.

◆ WhitenSystem() [4/4]

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

overridevirtual

Whiten a system, in place as well

Implements gtsam::noiseModel::Base.

Definition at line 217 of file NoiseModel.cpp.

Member Data Documentation

◆ sqrt_information_

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

protected

Matrix square root of information matrix (R)

Definition at line 173 of file NoiseModel.h.

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

Public Types

Public Member Functions

Static Public Member Functions

Protected Attributes

Private Member Functions

Detailed Description

Member Typedef Documentation

◆ shared_ptr

Constructor & Destructor Documentation

◆ Gaussian()

◆ ~Gaussian()

Member Function Documentation

◆ covariance()

◆ Covariance()

◆ equals()

◆ information()

◆ Information()

◆ logDeterminant()

◆ logDetR()

◆ negLogConstant()

◆ print()

◆ QR()

◆ R()

◆ sigmas()

◆ SqrtInformation()

◆ thisR()

◆ unwhiten()

◆ Whiten()

◆ whiten()

◆ WhitenInPlace() [1/2]

◆ WhitenInPlace() [2/2]

◆ WhitenSystem() [1/4]

◆ WhitenSystem() [2/4]

◆ WhitenSystem() [3/4]

◆ WhitenSystem() [4/4]

Member Data Documentation

◆ sqrt_information_