12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
gtsam.h
View File

@ -1458,7 +1458,7 @@ virtual class Null: gtsam::noiseModel::mEstimator::Base {
void serializable() const; void serializable() const;
double weight(double error) const; double weight(double error) const;
double residual(double error) const; double loss(double error) const;
}; };
virtual class Fair: gtsam::noiseModel::mEstimator::Base { virtual class Fair: gtsam::noiseModel::mEstimator::Base {
@ -1469,7 +1469,7 @@ virtual class Fair: gtsam::noiseModel::mEstimator::Base {
void serializable() const; void serializable() const;
double weight(double error) const; double weight(double error) const;
double residual(double error) const; double loss(double error) const;
}; };
virtual class Huber: gtsam::noiseModel::mEstimator::Base { virtual class Huber: gtsam::noiseModel::mEstimator::Base {
@ -1480,7 +1480,7 @@ virtual class Huber: gtsam::noiseModel::mEstimator::Base {
void serializable() const; void serializable() const;
double weight(double error) const; double weight(double error) const;
double residual(double error) const; double loss(double error) const;
}; };
virtual class Cauchy: gtsam::noiseModel::mEstimator::Base { virtual class Cauchy: gtsam::noiseModel::mEstimator::Base {
@ -1491,7 +1491,7 @@ virtual class Cauchy: gtsam::noiseModel::mEstimator::Base {
void serializable() const; void serializable() const;
double weight(double error) const; double weight(double error) const;
double residual(double error) const; double loss(double error) const;
}; };
virtual class Tukey: gtsam::noiseModel::mEstimator::Base { virtual class Tukey: gtsam::noiseModel::mEstimator::Base {
@ -1502,7 +1502,7 @@ virtual class Tukey: gtsam::noiseModel::mEstimator::Base {
void serializable() const; void serializable() const;
double weight(double error) const; double weight(double error) const;
double residual(double error) const; double loss(double error) const;
}; };
virtual class Welsch: gtsam::noiseModel::mEstimator::Base { virtual class Welsch: gtsam::noiseModel::mEstimator::Base {
@ -1513,7 +1513,7 @@ virtual class Welsch: gtsam::noiseModel::mEstimator::Base {
void serializable() const; void serializable() const;
double weight(double error) const; double weight(double error) const;
double residual(double error) const; double loss(double error) const;
}; };
virtual class GemanMcClure: gtsam::noiseModel::mEstimator::Base { virtual class GemanMcClure: gtsam::noiseModel::mEstimator::Base {
@ -1524,7 +1524,7 @@ virtual class GemanMcClure: gtsam::noiseModel::mEstimator::Base {
void serializable() const; void serializable() const;
double weight(double error) const; double weight(double error) const;
double residual(double error) const; double loss(double error) const;
}; };
virtual class DCS: gtsam::noiseModel::mEstimator::Base { virtual class DCS: gtsam::noiseModel::mEstimator::Base {
@ -1535,7 +1535,7 @@ virtual class DCS: gtsam::noiseModel::mEstimator::Base {
void serializable() const; void serializable() const;
double weight(double error) const; double weight(double error) const;
double residual(double error) const; double loss(double error) const;
}; };
virtual class L2WithDeadZone: gtsam::noiseModel::mEstimator::Base { virtual class L2WithDeadZone: gtsam::noiseModel::mEstimator::Base {
@ -1546,7 +1546,7 @@ virtual class L2WithDeadZone: gtsam::noiseModel::mEstimator::Base {
void serializable() const; void serializable() const;
double weight(double error) const; double weight(double error) const;
double residual(double error) const; double loss(double error) const;
}; };
}///\namespace mEstimator }///\namespace mEstimator

14
gtsam/linear/LossFunctions.h
View File

@ -36,12 +36,12 @@ namespace noiseModel {
* The mEstimator name space contains all robust error functions. * The mEstimator name space contains all robust error functions.
* It mirrors the exposition at * It mirrors the exposition at
* https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf * 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: * To illustrate, let's consider the least-squares (L2), L1, and Huber estimators as examples:
* *
* Name Symbol Least-Squares L1-norm Huber * 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 * 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 * 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 * the quadratic function for an L2 penalty, the absolute value function for
* an L1 penalty, etc. * an L1 penalty, etc.
* *
* TODO(mikebosse): When the residual function has as input the norm of the * TODO(mikebosse): When the loss function has as input the norm of the
* residual vector, then it prevents implementations of asymmeric loss * error vector, then it prevents implementations of asymmeric loss
* functions. It would be better for this function to accept the vector and * functions. It would be better for this function to accept the vector and
* internally call the norm if necessary. * 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 * This method is responsible for returning the weight function for a given
* amount of error. The weight function is related to the analytic derivative * 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 * https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf
* for details. This method is required when optimizing cost functions with * for details. This method is required when optimizing cost functions with
* robust penalties using iteratively re-weighted least squares. * 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 { class GTSAM_EXPORT Null : public Base {
public: public:
typedef boost::shared_ptr<Null> shared_ptr; 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(const ReweightScheme reweight = Block) : Base(reweight) {}
~Null() {} ~Null() {}
double weight(double /*error*/) const { return 1.0; } 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; void print(const std::string &s) const;
bool equals(const Base & /*expected*/, double /*tol*/) const { return true; } bool equals(const Base & /*expected*/, double /*tol*/) const { return true; }
static shared_ptr Create(); static shared_ptr Create();