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.

Inheritance diagram for gtsam::Gaussian:

Public Member Functions
	Gaussian (size_t dim=1, const std::optional< Matrix > &sqrt_information={})
	constructor takes square root information matrix
void	print (const std::string &name) const override
bool	equals (const Base &expected, double tol=1e-9) const override
Vector	sigmas () const override
	Calculate standard deviations.
Vector	whiten (const Vector &v) const override
	Whiten an error vector.
Vector	unwhiten (const Vector &v) const override
	Unwhiten an error vector.
void	unwhitenInPlace (Vector &v) const override
	in-place unwhiten, override if can be done more efficiently
void	unwhitenInPlace (Eigen::Block< Vector > &v) const override
	in-place unwhiten, override if can be done more efficiently
Matrix	Whiten (const Matrix &H) const override
	Multiply a derivative with R (derivative of whiten) Equivalent to whitening each column of the input matrix.
virtual void	WhitenInPlace (Matrix &H) const
	In-place version.
virtual void	WhitenInPlace (Eigen::Block< Matrix > H) const
	In-place version.
void	WhitenSystem (std::vector< Matrix > &A, Vector &b) const override
	Whiten a system, in place as well.
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 std::shared_ptr< Diagonal >	QR (Matrix &Ab) const
	Apply appropriately weighted QR factorization to the system [A b] Q' * [A b] = [R d] Dimensions: (rm) m(n+1) = r(n+1), where r = min(m,n).
virtual Matrix	R () const
	Return R itself, but note that Whiten(H) is cheaper than R*H.
virtual Matrix	information () const
	Compute information matrix.
virtual Matrix	covariance () const
	Compute covariance matrix.
double	logDeterminant () const
	Compute the log of \|Σ\|.
double	negLogConstant () const
	Compute the negative log of the normalization constant for a Gaussian noise model k = 1/\sqrt(\|2πΣ\|).
Public Member Functions inherited from gtsam::noiseModel::Base
	Base (size_t dim=1)
	primary constructor
virtual bool	isConstrained () const
	true if a constrained noise model, saves slow/clumsy dynamic casting
virtual bool	isUnit () const
	true if a unit noise model, saves slow/clumsy dynamic casting
size_t	dim () const
	Dimensionality.
virtual bool	equals (const Base &expected, double tol=1e-9) const =0
virtual double	squaredMahalanobisDistance (const Vector &v) const
	Squared Mahalanobis distance v'R'Rv = <Rv,R*v>.
virtual double	mahalanobisDistance (const Vector &v) const
	Mahalanobis distance.
virtual double	loss (const double squared_distance) const
	Loss function, input is squared Mahalanobis distance.
virtual double	loss (const Vector &v) const
	Evaluate the loss of an unwhitened residual v.
virtual void	whitenInPlace (Vector &v) const
	in-place whiten, override if can be done more efficiently
virtual void	whitenInPlace (Eigen::Block< Vector > &v) const
	in-place whiten, override if can be done more efficiently
virtual Vector	unweightedWhiten (const Vector &v) const
	Useful function for robust noise models to get the unweighted but whitened error.
virtual double	weight (const Vector &v) const
	get the weight from the effective loss function on residual vector v

Static Public Member Functions
static shared_ptr	SqrtInformation (const Matrix &R, bool smart=true)
	A Gaussian noise model created by specifying a square root information matrix.
static shared_ptr	Information (const Matrix &M, bool smart=true)
	A Gaussian noise model created by specifying an information matrix.
static shared_ptr	Covariance (const Matrix &covariance, bool smart=true)
	A Gaussian noise model created by specifying a covariance matrix.

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

Protected Attributes
std::optional< Matrix >	sqrt_information_
	Matrix square root of information matrix (R).
Protected Attributes inherited from gtsam::noiseModel::Base
size_t	dim_

Member Function Documentation

◆ 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

◆ 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

◆ 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

◆ print()

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

overridevirtual

Implements gtsam::noiseModel::Base.

◆ 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

◆ sigmas()

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

overridevirtual

Calculate standard deviations.

Reimplemented from gtsam::noiseModel::Base.

◆ 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

◆ unwhiten()

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

overridevirtual

Unwhiten an error vector.

Implements gtsam::noiseModel::Base.

◆ unwhitenInPlace() [1/2]

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

overridevirtual

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

Reimplemented from gtsam::noiseModel::Base.

◆ unwhitenInPlace() [2/2]

void gtsam::noiseModel::Gaussian::unwhitenInPlace ( Vector & v ) const

overridevirtual

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

Reimplemented from gtsam::noiseModel::Base.

◆ 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.

◆ whiten()

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

overridevirtual

Whiten an error vector.

Implements gtsam::noiseModel::Base.

◆ WhitenSystem() [1/4]

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

overridevirtual

Implements gtsam::noiseModel::Base.

◆ WhitenSystem() [2/4]

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

overridevirtual

Implements gtsam::noiseModel::Base.

◆ WhitenSystem() [3/4]

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

overridevirtual

Implements gtsam::noiseModel::Base.

◆ 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.

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

/tmp/gtsam-4.3.0-doxygen.rsXPUS/source/gtsam/linear/NoiseModel.h
/tmp/gtsam-4.3.0-doxygen.rsXPUS/source/gtsam/linear/NoiseModel.cpp

Detailed Description

Public Member Functions

Static Public Member Functions

Public Types

Protected Attributes

Member Function Documentation

◆ Covariance()

◆ Information()

◆ negLogConstant()

◆ print()

◆ QR()

◆ sigmas()

◆ SqrtInformation()

◆ unwhiten()

◆ unwhitenInPlace() [1/2]

◆ unwhitenInPlace() [2/2]

◆ Whiten()

◆ whiten()

◆ WhitenSystem() [1/4]

◆ WhitenSystem() [2/4]

◆ WhitenSystem() [3/4]

◆ WhitenSystem() [4/4]