bring cython gtsam.h closer to the original version for matlab
Duy-Nguyen Ta
parent
88b626a0dc
commit
5c5cc65951
333
cython/gtsam.h
333
cython/gtsam.h
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@ -1,3 +1,107 @@
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/**
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* GTSAM Wrap Module Definition
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*
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* These are the current classes available through the matlab toolbox interface,
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* add more functions/classes as they are available.
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*
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* Requirements:
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* Classes must start with an uppercase letter
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* - Can wrap a typedef
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* Only one Method/Constructor per line, though methods/constructors can extend across multiple lines
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* Methods can return
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* - Eigen types: Matrix, Vector
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* - C/C++ basic types: string, bool, size_t, int, double, char, unsigned char
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* - void
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* - Any class with which be copied with boost::make_shared()
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* - boost::shared_ptr of any object type
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* Constructors
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* - Overloads are supported
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* - A class with no constructors can be returned from other functions but not allocated directly in MATLAB
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* Methods
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* - Constness has no effect
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* - Specify by-value (not reference) return types, even if C++ method returns reference
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* - Must start with a letter (upper or lowercase)
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* - Overloads are supported
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* Static methods
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* - Must start with a letter (upper or lowercase) and use the "static" keyword
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* - The first letter will be made uppercase in the generated MATLAB interface
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* - Overloads are supported
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* Arguments to functions any of
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* - Eigen types: Matrix, Vector
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* - Eigen types and classes as an optionally const reference
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* - C/C++ basic types: string, bool, size_t, size_t, double, char, unsigned char
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* - Any class with which be copied with boost::make_shared() (except Eigen)
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* - boost::shared_ptr of any object type (except Eigen)
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* Comments can use either C++ or C style, with multiple lines
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* Namespace definitions
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* - Names of namespaces must start with a lowercase letter
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* - start a namespace with "namespace {"
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* - end a namespace with exactly "}"
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* - Namespaces can be nested
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* Namespace usage
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* - Namespaces can be specified for classes in arguments and return values
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* - In each case, the namespace must be fully specified, e.g., "namespace1::namespace2::ClassName"
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* Includes in C++ wrappers
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* - All includes will be collected and added in a single file
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* - All namespaces must have angle brackets: <path>
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* - No default includes will be added
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* Global/Namespace functions
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* - Functions specified outside of a class are global
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* - Can be overloaded with different arguments
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* - Can have multiple functions of the same name in different namespaces
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* Using classes defined in other modules
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* - If you are using a class 'OtherClass' not wrapped in this definition file, add "class OtherClass;" to avoid a dependency error
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* Virtual inheritance
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* - Specify fully-qualified base classes, i.e. "virtual class Derived : ns::Base {" where "ns" is the namespace
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* - Mark with 'virtual' keyword, e.g. "virtual class Base {", and also "virtual class Derived : ns::Base {"
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* - Forward declarations must also be marked virtual, e.g. "virtual class ns::Base;" and
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* also "virtual class ns::Derived;"
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* - Pure virtual (abstract) classes should list no constructors in this interface file
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* - Virtual classes must have a clone() function in C++ (though it does not have to be included
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* in the MATLAB interface). clone() will be called whenever an object copy is needed, instead
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* of using the copy constructor (which is used for non-virtual objects).
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* - Signature of clone function - will be called virtually, so must appear at least at the top of the inheritance tree
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* virtual boost::shared_ptr<CLASS_NAME> clone() const;
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* Class Templates
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* - Basic templates are supported either with an explicit list of types to instantiate,
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* e.g. template<T = {gtsam::Pose2, gtsam::Rot2, gtsam::Point3}> class Class1 { ... };
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* or with typedefs, e.g.
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* template<T, U> class Class2 { ... };
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* typedef Class2<Type1, Type2> MyInstantiatedClass;
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* - In the class definition, appearances of the template argument(s) will be replaced with their
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* instantiated types, e.g. 'void setValue(const T& value);'.
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* - To refer to the instantiation of the template class itself, use 'This', i.e. 'static This Create();'
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* - To create new instantiations in other modules, you must copy-and-paste the whole class definition
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* into the new module, but use only your new instantiation types.
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* - When forward-declaring template instantiations, use the generated/typedefed name, e.g.
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* class gtsam::Class1Pose2;
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* class gtsam::MyInstantiatedClass;
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* Boost.serialization within Matlab:
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* - you need to mark classes as being serializable in the markup file (see this file for an example).
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* - There are two options currently, depending on the class. To "mark" a class as serializable,
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* add a function with a particular signature so that wrap will catch it.
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* - Add "void serialize()" to a class to create serialization functions for a class.
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* Adding this flag subsumes the serializable() flag below. Requirements:
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* - A default constructor must be publicly accessible
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* - Must not be an abstract base class
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* - The class must have an actual boost.serialization serialize() function.
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* - Add "void serializable()" to a class if you only want the class to be serialized as a
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* part of a container (such as noisemodel). This version does not require a publicly
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* accessible default constructor.
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*/
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/**
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* Status:
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* - TODO: default values for arguments
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* - WORKAROUND: make multiple versions of the same function for different configurations of default arguments
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* - TODO: Handle gtsam::Rot3M conversions to quaternions
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* - TODO: Parse return of const ref arguments
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* - TODO: Parse std::string variants and convert directly to special string
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* - TODO: Add enum support
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* - TODO: Add generalized serialization support via boost.serialization with hooks to matlab save/load
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*/
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namespace gtsam {
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#include <gtsam/inference/Key.h>
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@ -35,6 +139,14 @@ template<K,V> class FastMap {
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FastMap(const This& f);
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};
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//*************************************************************************
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// base
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//*************************************************************************
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/** gtsam namespace functions */
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#include <gtsam/base/Matrix.h>
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bool linear_independent(Matrix A, Matrix B, double tol);
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#include <gtsam/base/Value.h>
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virtual class Value {
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// No constructors because this is an abstract class
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@ -578,7 +690,6 @@ virtual class Cal3DS2_Base {
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// Action on Point2
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gtsam::Point2 uncalibrate(const gtsam::Point2& p) const;
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gtsam::Point2 calibrate(const gtsam::Point2& p, double tol) const;
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// gtsam::Point2 calibrate(const gtsam::Point2& p) const;
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// enabling serialization functionality
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void serialize() const;
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@ -674,7 +785,6 @@ class Cal3Bundler {
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// Action on Point2
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gtsam::Point2 calibrate(const gtsam::Point2& p, double tol) const;
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gtsam::Point2 calibrate(const gtsam::Point2& p) const;
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gtsam::Point2 uncalibrate(const gtsam::Point2& p) const;
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// Standard Interface
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@ -848,30 +958,6 @@ gtsam::Point3 triangulatePoint3(const gtsam::Pose3Vector& poses,
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gtsam::Cal3Bundler* sharedCal, const gtsam::Point2Vector& measurements,
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double rank_tol, bool optimize);
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//*************************************************************************
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// Inference
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//*************************************************************************
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#include <gtsam/inference/Ordering.h>
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class Ordering {
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// Standard Constructors and Named Constructors
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Ordering();
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Ordering(const gtsam::Ordering& other);
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// Testable
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void print(string s) const;
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bool equals(const gtsam::Ordering& ord, double tol) const;
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// Standard interface
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size_t size() const;
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size_t at(size_t key) const;
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void push_back(size_t key);
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// enabling serialization functionality
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void serialize() const;
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};
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//*************************************************************************
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// Symbolic
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//*************************************************************************
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@ -912,7 +998,6 @@ virtual class SymbolicFactorGraph {
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// Standard interface
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gtsam::KeySet keys() const;
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// void push_back(gtsam::SymbolicFactor* factor);
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void push_back(const gtsam::SymbolicFactorGraph& graph);
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void push_back(const gtsam::SymbolicBayesNet& bayesNet);
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void push_back(const gtsam::SymbolicBayesTree& bayesTree);
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@ -1031,28 +1116,28 @@ class SymbolicBayesTree {
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// void deleteCachedShortcuts();
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// };
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// #include <gtsam/inference/VariableIndex.h>
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// class VariableIndex {
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// // Standard Constructors and Named Constructors
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// VariableIndex();
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// // TODO: Templetize constructor when wrap supports it
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// //template<T = {gtsam::FactorGraph}>
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// //VariableIndex(const T& factorGraph, size_t nVariables);
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// //VariableIndex(const T& factorGraph);
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// VariableIndex(const gtsam::SymbolicFactorGraph& factorGraph);
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// VariableIndex(const gtsam::GaussianFactorGraph& factorGraph);
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// VariableIndex(const gtsam::NonlinearFactorGraph& factorGraph);
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// VariableIndex(const gtsam::VariableIndex& other);
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#include <gtsam/inference/VariableIndex.h>
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class VariableIndex {
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// Standard Constructors and Named Constructors
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VariableIndex();
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// TODO: Templetize constructor when wrap supports it
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//template<T = {gtsam::FactorGraph}>
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//VariableIndex(const T& factorGraph, size_t nVariables);
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//VariableIndex(const T& factorGraph);
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VariableIndex(const gtsam::SymbolicFactorGraph& factorGraph);
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VariableIndex(const gtsam::GaussianFactorGraph& factorGraph);
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VariableIndex(const gtsam::NonlinearFactorGraph& factorGraph);
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VariableIndex(const gtsam::VariableIndex& other);
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// // Testable
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// bool equals(const gtsam::VariableIndex& other, double tol) const;
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// void print(string s) const;
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// Testable
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bool equals(const gtsam::VariableIndex& other, double tol) const;
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void print(string s) const;
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// // Standard interface
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// size_t size() const;
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// size_t nFactors() const;
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// size_t nEntries() const;
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// };
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// Standard interface
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size_t size() const;
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size_t nFactors() const;
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size_t nEntries() const;
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};
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//*************************************************************************
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// linear
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@ -1277,8 +1362,8 @@ virtual class JacobianFactor : gtsam::GaussianFactor {
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double error(const gtsam::VectorValues& c) const;
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//Standard Interface
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Matrix py_getA() const;
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Vector py_getb() const;
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Matrix getA() const;
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Vector getb() const;
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size_t rows() const;
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size_t cols() const;
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bool isConstrained() const;
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@ -1646,6 +1731,82 @@ unsigned char mrsymbolChr(size_t key);
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unsigned char mrsymbolLabel(size_t key);
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size_t mrsymbolIndex(size_t key);
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#include <gtsam/inference/Ordering.h>
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class Ordering {
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// Standard Constructors and Named Constructors
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Ordering();
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Ordering(const gtsam::Ordering& other);
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// Testable
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void print(string s) const;
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bool equals(const gtsam::Ordering& ord, double tol) const;
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// Standard interface
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size_t size() const;
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size_t at(size_t key) const;
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void push_back(size_t key);
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// enabling serialization functionality
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void serialize() const;
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};
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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class NonlinearFactorGraph {
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NonlinearFactorGraph();
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NonlinearFactorGraph(const gtsam::NonlinearFactorGraph& graph);
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// FactorGraph
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void print(string s) const;
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bool equals(const gtsam::NonlinearFactorGraph& fg, double tol) const;
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size_t size() const;
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bool empty() const;
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void remove(size_t i);
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size_t nrFactors() const;
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gtsam::NonlinearFactor* at(size_t idx) const;
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void push_back(const gtsam::NonlinearFactorGraph& factors);
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void push_back(gtsam::NonlinearFactor* factor);
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void add(gtsam::NonlinearFactor* factor);
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bool exists(size_t idx) const;
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gtsam::KeySet keys() const;
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// NonlinearFactorGraph
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double error(const gtsam::Values& values) const;
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double probPrime(const gtsam::Values& values) const;
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gtsam::Ordering orderingCOLAMD() const;
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// Ordering* orderingCOLAMDConstrained(const gtsam::Values& c, const std::map<gtsam::Key,int>& constraints) const;
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gtsam::GaussianFactorGraph* linearize(const gtsam::Values& values) const;
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gtsam::NonlinearFactorGraph clone() const;
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// enabling serialization functionality
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void serialize() const;
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};
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#include <gtsam/nonlinear/NonlinearFactor.h>
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virtual class NonlinearFactor {
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// Factor base class
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size_t size() const;
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gtsam::KeyVector keys() const;
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void print(string s) const;
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void printKeys(string s) const;
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// NonlinearFactor
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bool equals(const gtsam::NonlinearFactor& other, double tol) const;
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double error(const gtsam::Values& c) const;
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size_t dim() const;
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bool active(const gtsam::Values& c) const;
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gtsam::GaussianFactor* linearize(const gtsam::Values& c) const;
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gtsam::NonlinearFactor* clone() const;
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// gtsam::NonlinearFactor* rekey(const gtsam::KeyVector& newKeys) const; //FIXME: Conversion from KeyVector to std::vector does not happen
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};
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#include <gtsam/nonlinear/NonlinearFactor.h>
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virtual class NoiseModelFactor: gtsam::NonlinearFactor {
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bool equals(const gtsam::NoiseModelFactor& other, double tol) const;
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gtsam::noiseModel::Base* get_noiseModel() const; // deprecated by below
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gtsam::noiseModel::Base* noiseModel() const;
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Vector unwhitenedError(const gtsam::Values& x) const;
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Vector whitenedError(const gtsam::Values& x) const;
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};
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#include <gtsam/nonlinear/Values.h>
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class Values {
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Values();
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@ -1705,64 +1866,6 @@ class Values {
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double atDouble(size_t j) const;
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};
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#include <gtsam/nonlinear/NonlinearFactor.h>
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virtual class NonlinearFactor {
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// Factor base class
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size_t size() const;
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gtsam::KeyVector keys() const;
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void print(string s) const;
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void printKeys(string s) const;
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// NonlinearFactor
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bool equals(const gtsam::NonlinearFactor& other, double tol) const;
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double error(const gtsam::Values& c) const;
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size_t dim() const;
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bool active(const gtsam::Values& c) const;
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gtsam::GaussianFactor* linearize(const gtsam::Values& c) const;
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gtsam::NonlinearFactor* clone() const;
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// gtsam::NonlinearFactor* rekey(const gtsam::KeyVector& newKeys) const; //FIXME: Conversion from KeyVector to std::vector does not happen
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};
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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class NonlinearFactorGraph {
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NonlinearFactorGraph();
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NonlinearFactorGraph(const gtsam::NonlinearFactorGraph& graph);
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// FactorGraph
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void print(string s) const;
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bool equals(const gtsam::NonlinearFactorGraph& fg, double tol) const;
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size_t size() const;
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bool empty() const;
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void remove(size_t i);
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size_t nrFactors() const;
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gtsam::NonlinearFactor* at(size_t idx) const;
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void push_back(const gtsam::NonlinearFactorGraph& factors);
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void push_back(gtsam::NonlinearFactor* factor);
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void add(gtsam::NonlinearFactor* factor);
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bool exists(size_t idx) const;
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// gtsam::KeySet keys() const;
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// NonlinearFactorGraph
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double error(const gtsam::Values& values) const;
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double probPrime(const gtsam::Values& values) const;
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gtsam::Ordering orderingCOLAMD() const;
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// Ordering* orderingCOLAMDConstrained(const gtsam::Values& c, const std::map<gtsam::Key,int>& constraints) const;
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gtsam::GaussianFactorGraph* linearize(const gtsam::Values& values) const;
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gtsam::NonlinearFactorGraph clone() const;
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// enabling serialization functionality
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void serialize() const;
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};
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#include <gtsam/nonlinear/NonlinearFactor.h>
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virtual class NoiseModelFactor: gtsam::NonlinearFactor {
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bool equals(const gtsam::NoiseModelFactor& other, double tol) const;
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gtsam::noiseModel::Base* get_noiseModel() const; // deprecated by below
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gtsam::noiseModel::Base* noiseModel() const;
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Vector unwhitenedError(const gtsam::Values& x) const;
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Vector whitenedError(const gtsam::Values& x) const;
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};
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// // Actually a FastList<Key>
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// #include <gtsam/inference/Key.h>
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// class KeyList {
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@ -1855,14 +1958,6 @@ virtual class NoiseModelFactor: gtsam::NonlinearFactor {
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typedef gtsam::FastMap<gtsam::Key,int> KeyGroupMap;
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#include <gtsam/nonlinear/Marginals.h>
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class JointMarginal {
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JointMarginal(const JointMarginal& j);
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Matrix at(size_t iVariable, size_t jVariable) const;
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Matrix fullMatrix() const;
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void print(string s) const;
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void print() const;
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};
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class Marginals {
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Marginals(const gtsam::NonlinearFactorGraph& graph,
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const gtsam::Values& solution);
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@ -1875,7 +1970,13 @@ class Marginals {
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gtsam::JointMarginal jointMarginalInformation(const gtsam::KeyVector& variables) const;
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};
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class JointMarginal {
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JointMarginal(const JointMarginal& j);
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Matrix at(size_t iVariable, size_t jVariable) const;
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Matrix fullMatrix() const;
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void print(string s) const;
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void print() const;
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};
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#include <gtsam/nonlinear/LinearContainerFactor.h>
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virtual class LinearContainerFactor : gtsam::NonlinearFactor {
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