params parsed correctly

tests/testGncOptimizer.cpp

@@ -18,6 +18,7 @@
  */
 
 #include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
+#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
 #include <gtsam/nonlinear/NonlinearFactorGraph.h>
 #include <tests/smallExample.h>
 
@@ -37,25 +38,32 @@ public:
   // using BaseOptimizer = BaseOptimizerParameters::OptimizerType;
   GncParams(const BaseOptimizerParameters& baseOptimizerParams): baseOptimizerParams(baseOptimizerParams) {}
 
+  // default constructor
+  GncParams(): baseOptimizerParams() {}
+
   BaseOptimizerParameters baseOptimizerParams;
 
   /// any other specific GNC parameters:
 };
 
 /* ************************************************************************* */
-//template <class GncParameters>
+template<class GncParameters>
-//class GncOptimizer {
+class GncOptimizer {
-// public:
+public:
-//  // types etc
+  // types etc
-//
+
-// private:
+private:
-//  FG INITIAL GncParameters params_;
+  NonlinearFactorGraph nfg_;
-//
+  Values state_;
-// public:
+  GncParameters params_;
-//  GncOptimizer(FG, INITIAL, const GncParameters& params) : params(params) {
+
-//    // Check that all noise models are Gaussian
+public:
-//  }
+  GncOptimizer(const NonlinearFactorGraph& graph,
-//
+      const Values& initialValues, const GncParameters& params = GncParameters()) :
+        nfg_(graph), state_(initialValues), params_(params) {
+    // TODO: Check that all noise models are Gaussian
+  }
+
 //  Values optimize() const {
 //    NonlinearFactorGraph currentGraph = graph_;
 //  for (i : {1, 2, 3}) {
@@ -66,15 +74,15 @@ public:
 //    }
 //    graph_i = this->makeGraph(currentSolution);
 //  }
-//  }
+//}
-//
+
-//  NonlinearFactorGraph makeGraph(const Values& currentSolution) const {
+//NonlinearFactorGraph makeGraph(const Values& currentSolution) const {
 //  // calculate some weights
 //  this->calculateWeights();
 //  // copy the graph with new weights
 //
-//  }
+//}
-//};
+};
 
 ///* ************************************************************************* */
 //TEST(GncOptimizer, calculateWeights) {
@@ -84,6 +92,32 @@ public:
 //TEST(GncOptimizer, copyGraph) {
 //}
 
+/* ************************************************************************* */
+TEST(GncOptimizer, gncParamsConstructor) {
+
+  //check params are correctly parsed
+  LevenbergMarquardtParams lmParams;
+  GncParams<LevenbergMarquardtParams> gncParams1(lmParams);
+  CHECK(lmParams.equals(gncParams1.baseOptimizerParams));
+
+  // check also default constructor
+  GncParams<LevenbergMarquardtParams> gncParams1b;
+  CHECK(lmParams.equals(gncParams1b.baseOptimizerParams));
+
+  // and check params become different if we change lmParams
+  lmParams.setVerbosity("DELTA");
+  CHECK(!lmParams.equals(gncParams1.baseOptimizerParams));
+
+  // and same for GN
+  GaussNewtonParams gnParams;
+  GncParams<GaussNewtonParams> gncParams2(gnParams);
+  CHECK(gnParams.equals(gncParams2.baseOptimizerParams));
+
+  // check default constructor
+  GncParams<GaussNewtonParams> gncParams2b;
+  CHECK(gnParams.equals(gncParams2b.baseOptimizerParams));
+}
+
 /* ************************************************************************* */
 TEST(GncOptimizer, makeGraph) {
   // has to have Gaussian noise models !
@@ -95,7 +129,7 @@ TEST(GncOptimizer, makeGraph) {
 
   LevenbergMarquardtParams lmParams;
   GncParams<LevenbergMarquardtParams> gncParams(lmParams);
-//  auto gnc = GncOptimizer(fg, initial, gncParams);
+  auto gnc = GncOptimizer<GncParams<LevenbergMarquardtParams>>(fg, initial, gncParams);
 
 //  NonlinearFactorGraph actual = gnc.makeGraph(initial);
 }