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update gtsam:: namespace in sfm.i

release/4.3a0
Varun Agrawal 2024-06-28 16:17:20 -04:00
parent f6dbcb695d
commit 49ff90dda9
1 changed files with 19 additions and 19 deletions
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38
gtsam/sfm/sfm.i
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@ -81,7 +81,7 @@ virtual class ShonanFactor3 : gtsam::NoiseModelFactor {
ShonanFactor3(size_t key1, size_t key2, const gtsam::Rot3& R12, size_t p); ShonanFactor3(size_t key1, size_t key2, const gtsam::Rot3& R12, size_t p);
ShonanFactor3(size_t key1, size_t key2, const gtsam::Rot3& R12, size_t p, ShonanFactor3(size_t key1, size_t key2, const gtsam::Rot3& R12, size_t p,
gtsam::noiseModel::Base* model); gtsam::noiseModel::Base* model);
Vector evaluateError(const gtsam::SOn& Q1, const gtsam::SOn& Q2); gtsam::Vector evaluateError(const gtsam::SOn& Q1, const gtsam::SOn& Q2);
}; };
#include <gtsam/sfm/BinaryMeasurement.h> #include <gtsam/sfm/BinaryMeasurement.h>
@ -162,23 +162,23 @@ class ShonanAveraging2 {
gtsam::Rot2 measured(size_t i); gtsam::Rot2 measured(size_t i);
gtsam::KeyVector keys(size_t i); gtsam::KeyVector keys(size_t i);
// Matrix API (advanced use, debugging) // gtsam::Matrix API (advanced use, debugging)
Matrix denseD() const; gtsam::Matrix denseD() const;
Matrix denseQ() const; gtsam::Matrix denseQ() const;
Matrix denseL() const; gtsam::Matrix denseL() const;
// Matrix computeLambda_(Matrix S) const; // gtsam::Matrix computeLambda_(gtsam::Matrix S) const;
Matrix computeLambda_(const gtsam::Values& values) const; gtsam::Matrix computeLambda_(const gtsam::Values& values) const;
Matrix computeA_(const gtsam::Values& values) const; gtsam::Matrix computeA_(const gtsam::Values& values) const;
double computeMinEigenValue(const gtsam::Values& values) const; double computeMinEigenValue(const gtsam::Values& values) const;
gtsam::Values initializeWithDescent(size_t p, const gtsam::Values& values, gtsam::Values initializeWithDescent(size_t p, const gtsam::Values& values,
const Vector& minEigenVector, const gtsam::Vector& minEigenVector,
double minEigenValue) const; double minEigenValue) const;
// Advanced API // Advanced API
gtsam::NonlinearFactorGraph buildGraphAt(size_t p) const; gtsam::NonlinearFactorGraph buildGraphAt(size_t p) const;
gtsam::Values initializeRandomlyAt(size_t p) const; gtsam::Values initializeRandomlyAt(size_t p) const;
double costAt(size_t p, const gtsam::Values& values) const; double costAt(size_t p, const gtsam::Values& values) const;
pair<double, Vector> computeMinEigenVector(const gtsam::Values& values) const; pair<double, gtsam::Vector> computeMinEigenVector(const gtsam::Values& values) const;
bool checkOptimality(const gtsam::Values& values) const; bool checkOptimality(const gtsam::Values& values) const;
gtsam::LevenbergMarquardtOptimizer* createOptimizerAt( gtsam::LevenbergMarquardtOptimizer* createOptimizerAt(
size_t p, const gtsam::Values& initial); size_t p, const gtsam::Values& initial);
@ -212,23 +212,23 @@ class ShonanAveraging3 {
gtsam::Rot3 measured(size_t i); gtsam::Rot3 measured(size_t i);
gtsam::KeyVector keys(size_t i); gtsam::KeyVector keys(size_t i);
// Matrix API (advanced use, debugging) // gtsam::Matrix API (advanced use, debugging)
Matrix denseD() const; gtsam::Matrix denseD() const;
Matrix denseQ() const; gtsam::Matrix denseQ() const;
Matrix denseL() const; gtsam::Matrix denseL() const;
// Matrix computeLambda_(Matrix S) const; // gtsam::Matrix computeLambda_(gtsam::Matrix S) const;
Matrix computeLambda_(const gtsam::Values& values) const; gtsam::Matrix computeLambda_(const gtsam::Values& values) const;
Matrix computeA_(const gtsam::Values& values) const; gtsam::Matrix computeA_(const gtsam::Values& values) const;
double computeMinEigenValue(const gtsam::Values& values) const; double computeMinEigenValue(const gtsam::Values& values) const;
gtsam::Values initializeWithDescent(size_t p, const gtsam::Values& values, gtsam::Values initializeWithDescent(size_t p, const gtsam::Values& values,
const Vector& minEigenVector, const gtsam::Vector& minEigenVector,
double minEigenValue) const; double minEigenValue) const;
// Advanced API // Advanced API
gtsam::NonlinearFactorGraph buildGraphAt(size_t p) const; gtsam::NonlinearFactorGraph buildGraphAt(size_t p) const;
gtsam::Values initializeRandomlyAt(size_t p) const; gtsam::Values initializeRandomlyAt(size_t p) const;
double costAt(size_t p, const gtsam::Values& values) const; double costAt(size_t p, const gtsam::Values& values) const;
pair<double, Vector> computeMinEigenVector(const gtsam::Values& values) const; pair<double, gtsam::Vector> computeMinEigenVector(const gtsam::Values& values) const;
bool checkOptimality(const gtsam::Values& values) const; bool checkOptimality(const gtsam::Values& values) const;
gtsam::LevenbergMarquardtOptimizer* createOptimizerAt( gtsam::LevenbergMarquardtOptimizer* createOptimizerAt(
size_t p, const gtsam::Values& initial); size_t p, const gtsam::Values& initial);