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

Wrapped additional functions in factors and factor graphs, added factor heirarchy, fixed mistake in noisemodel heirarchy

release/4.3a0
Richard Roberts 2012-07-11 18:18:57 +00:00
parent 774fcb5ca9
commit 646457bfb2
1 changed files with 25 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

43
gtsam.h
View File

@ -720,14 +720,14 @@ virtual class Diagonal : gtsam::noiseModel::Gaussian {
void print(string s) const; void print(string s) const;
}; };
virtual class Isotropic : gtsam::noiseModel::Gaussian { virtual class Isotropic : gtsam::noiseModel::Diagonal {
static gtsam::noiseModel::Isotropic* Sigma(size_t dim, double sigma); static gtsam::noiseModel::Isotropic* Sigma(size_t dim, double sigma);
static gtsam::noiseModel::Isotropic* Variance(size_t dim, double varianace); static gtsam::noiseModel::Isotropic* Variance(size_t dim, double varianace);
static gtsam::noiseModel::Isotropic* Precision(size_t dim, double precision); static gtsam::noiseModel::Isotropic* Precision(size_t dim, double precision);
void print(string s) const; void print(string s) const;
}; };
virtual class Unit : gtsam::noiseModel::Gaussian { virtual class Unit : gtsam::noiseModel::Isotropic {
static gtsam::noiseModel::Unit* Create(size_t dim); static gtsam::noiseModel::Unit* Create(size_t dim);
void print(string s) const; void print(string s) const;
}; };
@ -782,7 +782,7 @@ class GaussianBayesNet {
void push_front(gtsam::GaussianConditional* conditional); void push_front(gtsam::GaussianConditional* conditional);
}; };
class GaussianFactor { virtual class GaussianFactor {
void print(string s) const; void print(string s) const;
bool equals(const gtsam::GaussianFactor& lf, double tol) const; bool equals(const gtsam::GaussianFactor& lf, double tol) const;
double error(const gtsam::VectorValues& c) const; double error(const gtsam::VectorValues& c) const;
@ -790,7 +790,7 @@ class GaussianFactor {
size_t size() const; size_t size() const;
}; };
class JacobianFactor { virtual class JacobianFactor : gtsam::GaussianFactor {
JacobianFactor(); JacobianFactor();
JacobianFactor(Vector b_in); JacobianFactor(Vector b_in);
JacobianFactor(size_t i1, Matrix A1, Vector b, JacobianFactor(size_t i1, Matrix A1, Vector b,
@ -810,7 +810,7 @@ class JacobianFactor {
gtsam::GaussianFactor* negate() const; gtsam::GaussianFactor* negate() const;
}; };
class HessianFactor { virtual class HessianFactor : gtsam::GaussianFactor {
HessianFactor(const gtsam::HessianFactor& gf); HessianFactor(const gtsam::HessianFactor& gf);
HessianFactor(); HessianFactor();
HessianFactor(size_t j, Matrix G, Vector g, double f); HessianFactor(size_t j, Matrix G, Vector g, double f);
@ -834,22 +834,19 @@ class GaussianFactorGraph {
GaussianFactorGraph(const gtsam::GaussianBayesNet& CBN); GaussianFactorGraph(const gtsam::GaussianBayesNet& CBN);
// From FactorGraph // From FactorGraph
void push_back(gtsam::GaussianFactor* factor);
void print(string s) const; void print(string s) const;
bool equals(const gtsam::GaussianFactorGraph& lfgraph, double tol) const; bool equals(const gtsam::GaussianFactorGraph& lfgraph, double tol) const;
size_t size() const; size_t size() const;
gtsam::GaussianFactor* at(size_t idx) const; gtsam::GaussianFactor* at(size_t idx) const;
// Building the graph // Building the graph
void add(gtsam::JacobianFactor* factor); void push_back(gtsam::GaussianFactor* factor);
// all these won't work as MATLAB can't handle overloading void add(Vector b);
// void add(Vector b); void add(size_t key1, Matrix A1, Vector b, const gtsam::noiseModel::Diagonal* model);
// void add(size_t key1, Matrix A1, Vector b, const gtsam::SharedDiagonal& model); void add(size_t key1, Matrix A1, size_t key2, Matrix A2, Vector b,
// void add(size_t key1, Matrix A1, size_t key2, Matrix A2, Vector b, const gtsam::noiseModel::Diagonal* model);
// const gtsam::SharedDiagonal& model); void add(size_t key1, Matrix A1, size_t key2, Matrix A2, size_t key3, Matrix A3,
// void add(size_t key1, Matrix A1, size_t key2, Matrix A2, size_t key3, Matrix A3, Vector b, const gtsam::noiseModel::Diagonal* model);
// Vector b, const gtsam::SharedDiagonal& model);
// void add(gtsam::HessianFactor* factor);
// error and probability // error and probability
double error(const gtsam::VectorValues& c) const; double error(const gtsam::VectorValues& c) const;
@ -951,15 +948,25 @@ class InvertedOrdering {
class NonlinearFactorGraph { class NonlinearFactorGraph {
NonlinearFactorGraph(); NonlinearFactorGraph();
void print(string s) const; void print(string s) const;
double error(const gtsam::Values& c) const;
double probPrime(const gtsam::Values& c) const;
gtsam::Ordering* orderingCOLAMD(const gtsam::Values& c) const;
// Ordering* orderingCOLAMDConstrained(const gtsam::Values& c, const std::map<gtsam::Key,int>& constraints) const;
gtsam::GaussianFactorGraph* linearize(const gtsam::Values& c, const gtsam::Ordering& ordering) const;
gtsam::NonlinearFactorGraph clone() const;
}; };
class NonlinearFactor { virtual class NonlinearFactor {
// NonlinearFactor(); // FIXME: don't use this - creates an abstract class
void print(string s) const; void print(string s) const;
void equals(const gtsam::NonlinearFactor& other, double tol) const; void equals(const gtsam::NonlinearFactor& other, double tol) const;
gtsam::KeyVector keys() const; gtsam::KeyVector keys() const;
size_t size() const; size_t size() const;
size_t dim() const; // FIXME: Doesn't link size_t dim() const;
double error(const gtsam::Values& c) const;
bool active(const gtsam::Values& c) const;
gtsam::GaussianFactor* linearize(const gtsam::Values& c, const gtsam::Ordering& ordering) const;
gtsam::NonlinearFactor* clone() const;
// gtsam::NonlinearFactor* rekey(const gtsam::KeyVector& newKeys) const; //FIXME: Conversion from KeyVector to std::vector does not happen
}; };
class Values { class Values {