#include <LossFunctions.h>

Inheritance diagram for gtsam::noiseModel::mEstimator::Base:

[legend]

Public Types
enum	ReweightScheme { Scalar, Block }

typedef std::shared_ptr< Base >	shared_ptr

Public Member Functions
	Base (const ReweightScheme reweight=Block)

ReweightScheme	reweightScheme () const
	Returns the reweight scheme, as explained in ReweightScheme.

virtual double	loss (double distance) const

virtual double	weight (double distance) const =0

virtual void	print (const std::string &s) const =0

virtual bool	equals (const Base &expected, double tol=1e-8) const =0

double	sqrtWeight (double distance) const

Vector	weight (const Vector &error) const

Vector	sqrtWeight (const Vector &error) const

void	reweight (Vector &error) const

void	reweight (std::vector< Matrix > &A, Vector &error) const

void	reweight (Matrix &A, Vector &error) const

void	reweight (Matrix &A1, Matrix &A2, Vector &error) const

void	reweight (Matrix &A1, Matrix &A2, Matrix &A3, Vector &error) const

Protected Attributes
ReweightScheme	reweight_
	Strategy for reweighting. More...

Detailed Description

Pure virtual class for all robust error function classes.

It provides the machinery for block vs scalar reweighting strategies, in addition to defining the interface of derived classes.

Member Enumeration Documentation

◆ ReweightScheme

enum gtsam::noiseModel::mEstimator::Base::ReweightScheme

the rows can be weighted independently according to the error or uniformly with the norm of the right hand side

Member Function Documentation

◆ loss()

virtual double gtsam::noiseModel::mEstimator::Base::loss ( double distance ) const

inlinevirtual

This method is responsible for returning the total penalty for a given amount of error. For example, this method is responsible for implementing the quadratic function for an L2 penalty, the absolute value function for an L1 penalty, etc.

TODO(mikebosse): When the loss function has as input the norm of the error vector, then it prevents implementations of asymmeric loss functions. It would be better for this function to accept the vector and internally call the norm if necessary.

This returns (x) in mEstimator

Reimplemented in gtsam::noiseModel::mEstimator::L2WithDeadZone, gtsam::noiseModel::mEstimator::DCS, gtsam::noiseModel::mEstimator::GemanMcClure, gtsam::noiseModel::mEstimator::Welsch, gtsam::noiseModel::mEstimator::Tukey, gtsam::noiseModel::mEstimator::Cauchy, gtsam::noiseModel::mEstimator::Huber, gtsam::noiseModel::mEstimator::Fair, and gtsam::noiseModel::mEstimator::Null.

◆ reweight()

void gtsam::noiseModel::mEstimator::Base::reweight ( Vector & error ) const

reweight block matrices and a vector according to their weight implementation

◆ sqrtWeight()

Vector gtsam::noiseModel::mEstimator::Base::sqrtWeight ( const Vector & error ) const

square root version of the weight function

◆ weight() [1/2]

virtual double gtsam::noiseModel::mEstimator::Base::weight ( double distance ) const

pure virtual

This method is responsible for returning the weight function for a given amount of error. The weight function is related to the analytic derivative of the loss function. See https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf for details. This method is required when optimizing cost functions with robust penalties using iteratively re-weighted least squares.

This returns w(x) in mEstimator

Implemented in gtsam::noiseModel::mEstimator::L2WithDeadZone, gtsam::noiseModel::mEstimator::DCS, gtsam::noiseModel::mEstimator::GemanMcClure, gtsam::noiseModel::mEstimator::Welsch, gtsam::noiseModel::mEstimator::Tukey, gtsam::noiseModel::mEstimator::Cauchy, gtsam::noiseModel::mEstimator::Huber, gtsam::noiseModel::mEstimator::Fair, and gtsam::noiseModel::mEstimator::Null.

◆ weight() [2/2]

Vector gtsam::noiseModel::mEstimator::Base::weight ( const Vector & error ) const

produce a weight vector according to an error vector and the implemented robust function

Member Data Documentation

◆ reweight_

ReweightScheme gtsam::noiseModel::mEstimator::Base::reweight_

protected

Strategy for reweighting.

See also: ReweightScheme

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/LossFunctions.h

Public Types

Public Member Functions

Protected Attributes