Public Types | Public Member Functions | Static Public Member Functions | Protected Attributes | List of all members
gtsam::noiseModel::mEstimator::Tukey Class Reference

#include <LossFunctions.h>

Inheritance diagram for gtsam::noiseModel::mEstimator::Tukey:
Inheritance graph
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Public Types

typedef std::shared_ptr< Tukeyshared_ptr
 
- Public Types inherited from gtsam::noiseModel::mEstimator::Base
enum  ReweightScheme { Scalar, Block }
 
typedef std::shared_ptr< Baseshared_ptr
 

Public Member Functions

bool equals (const Base &expected, double tol=1e-8) const override
 
double loss (double distance) const override
 
double modelParameter () const
 
void print (const std::string &s) const override
 
 Tukey (double c=4.6851, const ReweightScheme reweight=Block)
 
double weight (double distance) const override
 
- Public Member Functions inherited from gtsam::noiseModel::mEstimator::Base
 Base (const ReweightScheme reweight=Block)
 
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
 
ReweightScheme reweightScheme () const
 Returns the reweight scheme, as explained in ReweightScheme. More...
 
double sqrtWeight (double distance) const
 
Vector sqrtWeight (const Vector &error) const
 
Vector weight (const Vector &error) const
 
virtual ~Base ()
 

Static Public Member Functions

static shared_ptr Create (double k, const ReweightScheme reweight=Block)
 

Protected Attributes

double c_
 
double csquared_
 
- Protected Attributes inherited from gtsam::noiseModel::mEstimator::Base
ReweightScheme reweight_
 Strategy for reweighting. More...
 

Detailed Description

Implementation of the "Tukey" robust error model (Zhang97ivc).

This model has a scalar parameter "c".

Definition at line 292 of file LossFunctions.h.

Member Typedef Documentation

◆ shared_ptr

Definition at line 297 of file LossFunctions.h.

Constructor & Destructor Documentation

◆ Tukey()

gtsam::noiseModel::mEstimator::Tukey::Tukey ( double  c = 4.6851,
const ReweightScheme  reweight = Block 
)

Definition at line 243 of file LossFunctions.cpp.

Member Function Documentation

◆ Create()

Tukey::shared_ptr gtsam::noiseModel::mEstimator::Tukey::Create ( double  k,
const ReweightScheme  reweight = Block 
)
static

Definition at line 278 of file LossFunctions.cpp.

◆ equals()

bool gtsam::noiseModel::mEstimator::Tukey::equals ( const Base expected,
double  tol = 1e-8 
) const
overridevirtual

Implements gtsam::noiseModel::mEstimator::Base.

Definition at line 272 of file LossFunctions.cpp.

◆ loss()

double gtsam::noiseModel::mEstimator::Tukey::loss ( double  distance) const
overridevirtual

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 from gtsam::noiseModel::mEstimator::Base.

Definition at line 257 of file LossFunctions.cpp.

◆ modelParameter()

double gtsam::noiseModel::mEstimator::Tukey::modelParameter ( ) const
inline

Definition at line 305 of file LossFunctions.h.

◆ print()

void gtsam::noiseModel::mEstimator::Tukey::print ( const std::string &  s = "") const
overridevirtual

Implements gtsam::noiseModel::mEstimator::Base.

Definition at line 268 of file LossFunctions.cpp.

◆ weight()

double gtsam::noiseModel::mEstimator::Tukey::weight ( double  distance) const
overridevirtual

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

Implements gtsam::noiseModel::mEstimator::Base.

Definition at line 249 of file LossFunctions.cpp.

Member Data Documentation

◆ c_

double gtsam::noiseModel::mEstimator::Tukey::c_
protected

Definition at line 294 of file LossFunctions.h.

◆ csquared_

double gtsam::noiseModel::mEstimator::Tukey::csquared_
protected

Definition at line 294 of file LossFunctions.h.


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


gtsam
Author(s):
autogenerated on Tue Jul 4 2023 02:47:26