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params parsed correctly

release/4.3a0
lcarlone 2020-11-27 13:05:54 -05:00
parent ff40590fc3
commit 90dd2c7035
1 changed files with 62 additions and 28 deletions
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90
tests/testGncOptimizer.cpp
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@ -18,6 +18,7 @@
*/
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <tests/smallExample.h>
@ -37,44 +38,51 @@ 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>
//class GncOptimizer {
// public:
// // types etc
//
// private:
// FG INITIAL GncParameters params_;
//
// public:
// GncOptimizer(FG, INITIAL, const GncParameters& params) : params(params) {
// // Check that all noise models are Gaussian
// }
//
template<class GncParameters>
class GncOptimizer {
public:
// types etc
private:
NonlinearFactorGraph nfg_;
Values state_;
GncParameters params_;
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}) {
// BaseOptimizer::Optimizer baseOptimizer(currentGraph, initial);
// VALUES currentSolution = baseOptimizer.optimize();
// if (converged) {
// return currentSolution;
// }
// graph_i = this->makeGraph(currentSolution);
// for (i : {1, 2, 3}) {
// BaseOptimizer::Optimizer baseOptimizer(currentGraph, initial);
// VALUES currentSolution = baseOptimizer.optimize();
// if (converged) {
// return currentSolution;
// }
// graph_i = this->makeGraph(currentSolution);
// }
//}
//NonlinearFactorGraph makeGraph(const Values& currentSolution) const {
// // calculate some weights
// this->calculateWeights();
// // copy the graph with new weights
//
// 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);
}