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Removed commentted out and print-s

release/4.3a0
Fan Jiang 2020-03-01 19:38:57 -05:00
parent e312abdbf0
commit 3c0671ba8d
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GPG Key ID: 57E2B5DFA56B3012
1 changed files with 4 additions and 30 deletions
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34
tests/testNonlinearOptimizer.cpp
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@ -364,9 +364,8 @@ TEST(NonlinearOptimizer, Pose2OptimizationWithHuberNoOutlier) {
lm_params.setAbsoluteErrorTol(0); lm_params.setAbsoluteErrorTol(0);
lm_params.setMaxIterations(100); lm_params.setMaxIterations(100);
lm_params.setlambdaUpperBound(1e10); lm_params.setlambdaUpperBound(1e10);
// lm_params.setVerbosityLM("TRYLAMBDA");
gtsam::NonlinearConjugateGradientOptimizer::Parameters cg_params; NonlinearConjugateGradientOptimizer::Parameters cg_params;
cg_params.setErrorTol(0); cg_params.setErrorTol(0);
cg_params.setMaxIterations(100000); cg_params.setMaxIterations(100000);
cg_params.setRelativeErrorTol(0); cg_params.setRelativeErrorTol(0);
@ -377,8 +376,6 @@ TEST(NonlinearOptimizer, Pose2OptimizationWithHuberNoOutlier) {
dl_params.setAbsoluteErrorTol(0); dl_params.setAbsoluteErrorTol(0);
dl_params.setMaxIterations(100); dl_params.setMaxIterations(100);
// cg_params.setVerbosity("ERROR");
auto cg_result = NonlinearConjugateGradientOptimizer(fg, init, cg_params).optimize(); auto cg_result = NonlinearConjugateGradientOptimizer(fg, init, cg_params).optimize();
auto gn_result = GaussNewtonOptimizer(fg, init).optimize(); auto gn_result = GaussNewtonOptimizer(fg, init).optimize();
auto lm_result = LevenbergMarquardtOptimizer(fg, init, lm_params).optimize(); auto lm_result = LevenbergMarquardtOptimizer(fg, init, lm_params).optimize();
@ -414,19 +411,11 @@ TEST(NonlinearOptimizer, Point2LinearOptimizationWithHuber) {
expected.insert(1, Point2(1,1.85)); expected.insert(1, Point2(1,1.85));
LevenbergMarquardtParams params; LevenbergMarquardtParams params;
gtsam::NonlinearConjugateGradientOptimizer::Parameters cg_params; NonlinearConjugateGradientOptimizer::Parameters cg_params;
cg_params.setErrorTol(0);
cg_params.setMaxIterations(100000);
cg_params.setRelativeErrorTol(0);
cg_params.setAbsoluteErrorTol(0);
// cg_params.setVerbosity("ERROR");
auto cg_result = NonlinearConjugateGradientOptimizer(fg, init, cg_params).optimize(); auto cg_result = NonlinearConjugateGradientOptimizer(fg, init, cg_params).optimize();
auto gn_result = GaussNewtonOptimizer(fg, init).optimize(); auto gn_result = GaussNewtonOptimizer(fg, init).optimize();
auto lm_result = LevenbergMarquardtOptimizer(fg, init, params).optimize(); auto lm_result = LevenbergMarquardtOptimizer(fg, init, params).optimize();
auto dl_result = DoglegOptimizer(fg, init).optimize(); auto dl_result = DoglegOptimizer(fg, init).optimize();
EXPECT(assert_equal(expected, gn_result, 1e-4)); EXPECT(assert_equal(expected, gn_result, 1e-4));
@ -468,13 +457,9 @@ TEST(NonlinearOptimizer, Pose2OptimizationWithHuber) {
cg_params.setRelativeErrorTol(0); cg_params.setRelativeErrorTol(0);
cg_params.setAbsoluteErrorTol(0); cg_params.setAbsoluteErrorTol(0);
// cg_params.setVerbosity("ERROR");
auto cg_result = NonlinearConjugateGradientOptimizer(fg, init, cg_params).optimize(); auto cg_result = NonlinearConjugateGradientOptimizer(fg, init, cg_params).optimize();
fg.printErrors(cg_result);
auto gn_result = GaussNewtonOptimizer(fg, init).optimize(); auto gn_result = GaussNewtonOptimizer(fg, init).optimize();
auto lm_result = LevenbergMarquardtOptimizer(fg, init, params).optimize(); auto lm_result = LevenbergMarquardtOptimizer(fg, init, params).optimize();
auto dl_result = DoglegOptimizer(fg, init).optimize(); auto dl_result = DoglegOptimizer(fg, init).optimize();
EXPECT(assert_equal(expected, gn_result, 1e-1)); EXPECT(assert_equal(expected, gn_result, 1e-1));
@ -504,7 +489,6 @@ TEST(NonlinearOptimizer, RobustMeanCalculation) {
expected.insert(0, 3.33333333); expected.insert(0, 3.33333333);
LevenbergMarquardtParams params; LevenbergMarquardtParams params;
// params.setVerbosityLM("TRYLAMBDA");
params.setAbsoluteErrorTol(1e-20); params.setAbsoluteErrorTol(1e-20);
params.setRelativeErrorTol(1e-20); params.setRelativeErrorTol(1e-20);
@ -513,22 +497,12 @@ TEST(NonlinearOptimizer, RobustMeanCalculation) {
cg_params.setMaxIterations(10000); cg_params.setMaxIterations(10000);
cg_params.setRelativeErrorTol(0); cg_params.setRelativeErrorTol(0);
cg_params.setAbsoluteErrorTol(0); cg_params.setAbsoluteErrorTol(0);
// cg_params.setVerbosity("ERROR");
auto cg_result = NonlinearConjugateGradientOptimizer(fg, init, cg_params).optimize(); auto cg_result = NonlinearConjugateGradientOptimizer(fg, init, cg_params).optimize();
// cg_result.print("CG: ");
// cout << fg.error(cg_result) << endl << endl << endl;
auto gn_result = GaussNewtonOptimizer(fg, init).optimize(); auto gn_result = GaussNewtonOptimizer(fg, init).optimize();
// gn_result.print("GN: ");
// cout << fg.error(gn_result) << endl << endl << endl;
auto lm_result = LevenbergMarquardtOptimizer(fg, init, params).optimize(); auto lm_result = LevenbergMarquardtOptimizer(fg, init, params).optimize();
// lm_result.print("LM: ");
// cout << fg.error(lm_result) << endl << endl << endl;
auto dl_result = DoglegOptimizer(fg, init).optimize(); auto dl_result = DoglegOptimizer(fg, init).optimize();
// dl_result.print("DL: ");
// cout << fg.error(dl_result) << endl << endl << endl;
EXPECT(assert_equal(expected, gn_result, tol)); EXPECT(assert_equal(expected, gn_result, tol));
EXPECT(assert_equal(expected, gn_result, tol)); EXPECT(assert_equal(expected, gn_result, tol));
EXPECT(assert_equal(expected, lm_result, tol)); EXPECT(assert_equal(expected, lm_result, tol));