#include <LossFunctions.h>
Public Types | |
| typedef std::shared_ptr< Null > | shared_ptr |
 Public Types inherited from gtsam::noiseModel::mEstimator::Base | |
| enum | ReweightScheme { Scalar, Block } |
| typedef std::shared_ptr< Base > | shared_ptr |
Public Member Functions | |
| bool | equals (const Base &, double) const override |
| double | loss (double distance) const override |
| Null (const ReweightScheme reweight=Block) | |
| void | print (const std::string &s) const override |
| double | weight (double) const override |
| ~Null () 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 () |
Additional Inherited Members | |
| Protected Attributes inherited from gtsam::noiseModel::mEstimator::Base | |
| ReweightScheme | reweight_ |
| Strategy for reweighting. More... | |
Detailed Description
"Null" robust loss function, equivalent to a Gaussian pdf noise model, or plain least-squares (non-robust).
This model has no additional parameters.
- Loss (x) = 0.5 x²
- Derivative (x) = x
- Weight w(x) = (x)/x = 1
Definition at line 150 of file LossFunctions.h.
Member Typedef Documentation
◆ shared_ptr
| typedef std::shared_ptr<Null> gtsam::noiseModel::mEstimator::Null::shared_ptr |
Definition at line 152 of file LossFunctions.h.
Constructor & Destructor Documentation
◆ Null()
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inline |
Definition at line 154 of file LossFunctions.h.
◆ ~Null()
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inlineoverride |
Definition at line 155 of file LossFunctions.h.
Member Function Documentation
◆ Create()
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static |
Definition at line 132 of file LossFunctions.cpp.
◆ equals()
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inlineoverridevirtual |
Implements gtsam::noiseModel::mEstimator::Base.
Definition at line 159 of file LossFunctions.h.
◆ loss()
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inlineoverridevirtual |
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 157 of file LossFunctions.h.
◆ print()
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overridevirtual |
Implements gtsam::noiseModel::mEstimator::Base.
Definition at line 129 of file LossFunctions.cpp.
◆ weight()
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inlineoverridevirtual |
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 156 of file LossFunctions.h.
The documentation for this class was generated from the following files: