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rename residual to loss

release/4.3a0
yetongumich 2020-04-06 10:10:46 -04:00
parent 646a4b7f0f
commit efcc5c908e
2 changed files with 16 additions and 16 deletions
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18
gtsam.h
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@ -1458,7 +1458,7 @@ virtual class Null: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class Fair: gtsam::noiseModel::mEstimator::Base {
@ -1469,7 +1469,7 @@ virtual class Fair: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class Huber: gtsam::noiseModel::mEstimator::Base {
@ -1480,7 +1480,7 @@ virtual class Huber: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class Cauchy: gtsam::noiseModel::mEstimator::Base {
@ -1491,7 +1491,7 @@ virtual class Cauchy: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class Tukey: gtsam::noiseModel::mEstimator::Base {
@ -1502,7 +1502,7 @@ virtual class Tukey: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class Welsch: gtsam::noiseModel::mEstimator::Base {
@ -1513,7 +1513,7 @@ virtual class Welsch: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class GemanMcClure: gtsam::noiseModel::mEstimator::Base {
@ -1524,7 +1524,7 @@ virtual class GemanMcClure: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class DCS: gtsam::noiseModel::mEstimator::Base {
@ -1535,7 +1535,7 @@ virtual class DCS: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
virtual class L2WithDeadZone: gtsam::noiseModel::mEstimator::Base {
@ -1546,7 +1546,7 @@ virtual class L2WithDeadZone: gtsam::noiseModel::mEstimator::Base {
void serializable() const;
double weight(double error) const;
double residual(double error) const;
double loss(double error) const;
};
}///\namespace mEstimator

14
gtsam/linear/LossFunctions.h
View File

@ -36,12 +36,12 @@ namespace noiseModel {
* The mEstimator name space contains all robust error functions.
* It mirrors the exposition at
* https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf
* which talks about minimizing \sum \rho(r_i), where \rho is a residual function of choice.
* which talks about minimizing \sum \rho(r_i), where \rho is a loss function of choice.
*
* To illustrate, let's consider the least-squares (L2), L1, and Huber estimators as examples:
*
* Name Symbol Least-Squares L1-norm Huber
* Residual \rho(x) 0.5*x^2 |x| 0.5*x^2 if |x|<k, 0.5*k^2 + k|x-k| otherwise
* Loss \rho(x) 0.5*x^2 |x| 0.5*x^2 if |x|<k, 0.5*k^2 + k|x-k| otherwise
* Derivative \phi(x) x sgn(x) x if |x|<k, k sgn(x) otherwise
* Weight w(x)=\phi(x)/x 1 1/|x| 1 if |x|<k, k/|x| otherwise
*
@ -75,8 +75,8 @@ class GTSAM_EXPORT Base {
* the quadratic function for an L2 penalty, the absolute value function for
* an L1 penalty, etc.
*
* TODO(mikebosse): When the residual function has as input the norm of the
* residual vector, then it prevents implementations of asymmeric loss
* 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.
*/
@ -89,7 +89,7 @@ class GTSAM_EXPORT Base {
/*
* 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 residual function. See
* 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.
@ -127,7 +127,7 @@ class GTSAM_EXPORT Base {
}
};
/// Null class is not robust so is a Gaussian ?
/// Null class should behave as Gaussian
class GTSAM_EXPORT Null : public Base {
public:
typedef boost::shared_ptr<Null> shared_ptr;
@ -135,7 +135,7 @@ class GTSAM_EXPORT Null : public Base {
Null(const ReweightScheme reweight = Block) : Base(reweight) {}
~Null() {}
double weight(double /*error*/) const { return 1.0; }
double residual(double error) const { return error; }
double loss(double distance) const { return 0.5 * distance * distance; }
void print(const std::string &s) const;
bool equals(const Base & /*expected*/, double /*tol*/) const { return true; }
static shared_ptr Create();