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enable all Key containers

release/4.3a0
Duy-Nguyen Ta 2016-09-12 18:46:41 -04:00
parent 53dbe25c50
commit 3115f9b671
1 changed files with 24 additions and 15 deletions
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39
wrap/tests/cythontest.h
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@ -12,15 +12,24 @@ template<T> class FastVector {
typedef gtsam::FastVector<gtsam::Key> KeyVector; typedef gtsam::FastVector<gtsam::Key> KeyVector;
#include <gtsam/base/FastList.h> #include <gtsam/base/FastList.h>
template<T> class FastList{}; template<T> class FastList {
FastList();
FastList(const This& f);
};
typedef gtsam::FastList<gtsam::Key> KeyList; typedef gtsam::FastList<gtsam::Key> KeyList;
#include <gtsam/base/FastSet.h> #include <gtsam/base/FastSet.h>
template<T> class FastSet{}; template<T> class FastSet {
FastSet();
FastSet(const This& f);
};
typedef gtsam::FastSet<gtsam::Key> KeySet; typedef gtsam::FastSet<gtsam::Key> KeySet;
#include <gtsam/base/FastMap.h> #include <gtsam/base/FastMap.h>
template<K,V> class FastMap{}; template<K,V> class FastMap {
FastMap();
FastMap(const This& f);
};
#include <gtsam/base/Value.h> #include <gtsam/base/Value.h>
virtual class Value { virtual class Value {
@ -874,7 +883,7 @@ virtual class SymbolicFactorGraph {
bool exists(size_t idx) const; bool exists(size_t idx) const;
// Standard interface // Standard interface
// gtsam::KeySet keys() const; gtsam::KeySet keys() const;
// void push_back(gtsam::SymbolicFactor* factor); // void push_back(gtsam::SymbolicFactor* factor);
void push_back(const gtsam::SymbolicFactorGraph& graph); void push_back(const gtsam::SymbolicFactorGraph& graph);
void push_back(const gtsam::SymbolicBayesNet& bayesNet); void push_back(const gtsam::SymbolicBayesNet& bayesNet);
@ -1191,7 +1200,7 @@ class VectorValues {
#include <gtsam/linear/GaussianFactor.h> #include <gtsam/linear/GaussianFactor.h>
virtual class GaussianFactor { virtual class GaussianFactor {
// gtsam::KeyVector keys() const; gtsam::KeyVector keys() const;
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;
@ -1292,7 +1301,7 @@ class GaussianFactorGraph {
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;
// gtsam::KeySet keys() const; gtsam::KeySet keys() const;
bool exists(size_t idx) const; bool exists(size_t idx) const;
// Building the graph // Building the graph
@ -1330,19 +1339,19 @@ class GaussianFactorGraph {
gtsam::GaussianBayesTree* eliminateMultifrontal(const gtsam::Ordering& ordering); gtsam::GaussianBayesTree* eliminateMultifrontal(const gtsam::Ordering& ordering);
pair<gtsam::GaussianBayesNet*, gtsam::GaussianFactorGraph*> eliminatePartialSequential( pair<gtsam::GaussianBayesNet*, gtsam::GaussianFactorGraph*> eliminatePartialSequential(
const gtsam::Ordering& ordering); const gtsam::Ordering& ordering);
// pair<gtsam::GaussianBayesNet*, gtsam::GaussianFactorGraph*> eliminatePartialSequential( pair<gtsam::GaussianBayesNet*, gtsam::GaussianFactorGraph*> eliminatePartialSequential(
// const gtsam::KeyVector& keys); const gtsam::KeyVector& keys);
pair<gtsam::GaussianBayesTree*, gtsam::GaussianFactorGraph*> eliminatePartialMultifrontal( pair<gtsam::GaussianBayesTree*, gtsam::GaussianFactorGraph*> eliminatePartialMultifrontal(
const gtsam::Ordering& ordering); const gtsam::Ordering& ordering);
// pair<gtsam::GaussianBayesTree*, gtsam::GaussianFactorGraph*> eliminatePartialMultifrontal( pair<gtsam::GaussianBayesTree*, gtsam::GaussianFactorGraph*> eliminatePartialMultifrontal(
// const gtsam::KeyVector& keys); const gtsam::KeyVector& keys);
gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::Ordering& variables); gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::Ordering& variables);
// gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::KeyVector& variables); gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::KeyVector& variables);
gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::Ordering& variables, gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::Ordering& variables,
const gtsam::Ordering& marginalizedVariableOrdering); const gtsam::Ordering& marginalizedVariableOrdering);
// gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::KeyVector& variables, gtsam::GaussianBayesNet* marginalMultifrontalBayesNet(const gtsam::KeyVector& variables,
// const gtsam::Ordering& marginalizedVariableOrdering); const gtsam::Ordering& marginalizedVariableOrdering);
// gtsam::GaussianFactorGraph* marginal(const gtsam::KeyVector& variables); gtsam::GaussianFactorGraph* marginal(const gtsam::KeyVector& variables);
// Conversion to matrices // Conversion to matrices
Matrix sparseJacobian_() const; Matrix sparseJacobian_() const;
@ -1414,7 +1423,7 @@ virtual class GaussianBayesNet {
// FactorGraph derived interface // FactorGraph derived interface
// size_t size() const; // size_t size() const;
gtsam::GaussianConditional* at(size_t idx) const; gtsam::GaussianConditional* at(size_t idx) const;
// gtsam::KeySet keys() const; gtsam::KeySet keys() const;
bool exists(size_t idx) const; bool exists(size_t idx) const;
gtsam::GaussianConditional* front() const; gtsam::GaussianConditional* front() const;