gtsam::noiseModel::Diagonal Class Reference

Detailed Description

A diagonal noise model implements a diagonal covariance matrix, with the elements of the diagonal specified in a Vector.

This class has no public constructors, instead, use the static constructor functions Sigmas etc...

Inheritance diagram for gtsam::noiseModel::Diagonal:

Public Member Functions
	Diagonal ()
	constructor - no initializations, for serialization
void	print (const std::string &name) const override
Vector	sigmas () const override
	Calculate standard deviations. More...
Vector	whiten (const Vector &v) const override
	Whiten an error vector. More...
Vector	unwhiten (const Vector &v) const override
	Unwhiten an error vector. More...
Matrix	Whiten (const Matrix &H) const override
	Multiply a derivative with R (derivative of whiten) Equivalent to whitening each column of the input matrix. More...
void	WhitenInPlace (Matrix &H) const override
	In-place version. More...
void	WhitenInPlace (Eigen::Block< Matrix > H) const override
	In-place version. More...
double	sigma (size_t i) const
	Return standard deviations (sqrt of diagonal)
const Vector &	invsigmas () const
	Return sqrt precisions.
double	invsigma (size_t i) const
const Vector &	precisions () const
	Return precisions.
double	precision (size_t i) const
Matrix	R () const override
	Return R itself, but note that Whiten(H) is cheaper than R*H. More...
Public Member Functions inherited from gtsam::noiseModel::Gaussian
	Gaussian (size_t dim=1, const boost::optional< Matrix > &sqrt_information=boost::none)
	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. More...
Vector	whiten (const Vector &v) const override
	Whiten an error vector. More...
Vector	unwhiten (const Vector &v) const override
	Unwhiten an error vector. More...
Matrix	Whiten (const Matrix &H) const override
	Multiply a derivative with R (derivative of whiten) Equivalent to whitening each column of the input matrix. More...
virtual void	WhitenInPlace (Matrix &H) const
	In-place version. More...
virtual void	WhitenInPlace (Eigen::Block< Matrix > H) const
	In-place version. More...
void	WhitenSystem (std::vector< Matrix > &A, Vector &b) const override
	Whiten a system, in place as well. More...
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 boost::shared_ptr< Diagonal >	QR (Matrix &Ab) const
	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). More...
virtual Matrix	R () const
	Return R itself, but note that Whiten(H) is cheaper than R*H. More...
virtual Matrix	information () const
	Compute information matrix.
virtual Matrix	covariance () const
	Compute covariance matrix.
Public Member Functions inherited from gtsam::noiseModel::Base
	Base (size_t dim=1)
	primary constructor 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...
size_t	dim () const
	Dimensionality.
virtual void	print (const std::string &name="") const =0
virtual bool	equals (const Base &expected, double tol=1e-9) const =0
virtual Vector	sigmas () const
	Calculate standard deviations. More...
virtual Vector	whiten (const Vector &v) const =0
	Whiten an error vector. More...
virtual Matrix	Whiten (const Matrix &H) const =0
	Whiten a matrix. More...
virtual Vector	unwhiten (const Vector &v) const =0
	Unwhiten an error vector. More...
virtual double	squaredMahalanobisDistance (const Vector &v) const
	Squared Mahalanobis distance v'*R'*R*v = <R*v,R*v> More...
virtual double	mahalanobisDistance (const Vector &v) const
	Mahalanobis distance.
virtual double	loss (const double squared_distance) const
	loss function, input is Mahalanobis distance More...
virtual void	WhitenSystem (std::vector< Matrix > &A, Vector &b) const =0
virtual void	WhitenSystem (Matrix &A, Vector &b) const =0
virtual void	WhitenSystem (Matrix &A1, Matrix &A2, Vector &b) const =0
virtual void	WhitenSystem (Matrix &A1, Matrix &A2, Matrix &A3, Vector &b) const =0
virtual void	whitenInPlace (Vector &v) const
	in-place whiten, override if can be done more efficiently More...
virtual void	unwhitenInPlace (Vector &v) const
	in-place unwhiten, override if can be done more efficiently More...
virtual void	whitenInPlace (Eigen::Block< Vector > &v) const
	in-place whiten, override if can be done more efficiently More...
virtual void	unwhitenInPlace (Eigen::Block< Vector > &v) const
	in-place unwhiten, override if can be done more efficiently More...
virtual Vector	unweightedWhiten (const Vector &v) const
	Useful function for robust noise models to get the unweighted but whitened error. More...
virtual double	weight (const Vector &v) const
	get the weight from the effective loss function on residual vector v More...

Static Public Member Functions
static shared_ptr	Sigmas (const Vector &sigmas, bool smart=true)
	A diagonal noise model created by specifying a Vector of sigmas, i.e. More...
static shared_ptr	Variances (const Vector &variances, bool smart=true)
	A diagonal noise model created by specifying a Vector of variances, i.e. More...
static shared_ptr	Precisions (const Vector &precisions, bool smart=true)
	A diagonal noise model created by specifying a Vector of precisions, i.e. More...
Static Public Member Functions inherited from gtsam::noiseModel::Gaussian
static shared_ptr	SqrtInformation (const Matrix &R, bool smart=true)
	A Gaussian noise model created by specifying a square root information matrix. More...
static shared_ptr	Information (const Matrix &M, bool smart=true)
	A Gaussian noise model created by specifying an information matrix. More...
static shared_ptr	Covariance (const Matrix &covariance, bool smart=true)
	A Gaussian noise model created by specifying a covariance matrix. More...

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

Protected Member Functions
	Diagonal (const Vector &sigmas)
	constructor to allow for disabling initialization of invsigmas

Protected Attributes
Vector	sigmas_
	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.
Vector	invsigmas_
Vector	precisions_
Protected Attributes inherited from gtsam::noiseModel::Gaussian
boost::optional< Matrix >	sqrt_information_
	Matrix square root of information matrix (R)
Protected Attributes inherited from gtsam::noiseModel::Base
size_t	dim_

Friends
class	boost::serialization::access
	Serialization function.

Member Function Documentation

Precisions()

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

inlinestatic

A diagonal noise model created by specifying a Vector of precisions, i.e.

i.e. the diagonal of the information matrix, i.e., weights

print()

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

overridevirtual

Reimplemented from gtsam::noiseModel::Gaussian.

R()

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

inlineoverridevirtual

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

Reimplemented from gtsam::noiseModel::Gaussian.

sigmas()

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

inlineoverridevirtual

Calculate standard deviations.

Reimplemented from gtsam::noiseModel::Gaussian.

Sigmas()

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

static

A diagonal noise model created by specifying a Vector of sigmas, i.e.

standard deviations, the diagonal of the square root covariance matrix.

unwhiten()

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

overridevirtual

Unwhiten an error vector.

Reimplemented from gtsam::noiseModel::Gaussian.

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

Variances()

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

static

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

Whiten()

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

overridevirtual

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

Reimplemented from gtsam::noiseModel::Gaussian.

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

whiten()

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

overridevirtual

Whiten an error vector.

Reimplemented from gtsam::noiseModel::Gaussian.

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

WhitenInPlace() [1/2]

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

overridevirtual

In-place version.

Reimplemented from gtsam::noiseModel::Gaussian.

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

WhitenInPlace() [2/2]

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

overridevirtual

In-place version.

Reimplemented from gtsam::noiseModel::Gaussian.

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

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

• /Users/dellaert/git/github/gtsam/linear/NoiseModel.h
• /Users/dellaert/git/github/gtsam/linear/NoiseModel.cpp