Removed commentted out and print-s
							parent
							
								
									e312abdbf0
								
							
						
					
					
						commit
						3c0671ba8d
					
				|  | @ -364,9 +364,8 @@ TEST(NonlinearOptimizer, Pose2OptimizationWithHuberNoOutlier) { | |||
|   lm_params.setAbsoluteErrorTol(0); | ||||
|   lm_params.setMaxIterations(100); | ||||
|   lm_params.setlambdaUpperBound(1e10); | ||||
|   // lm_params.setVerbosityLM("TRYLAMBDA");
 | ||||
| 
 | ||||
|   gtsam::NonlinearConjugateGradientOptimizer::Parameters cg_params; | ||||
|   NonlinearConjugateGradientOptimizer::Parameters cg_params; | ||||
|   cg_params.setErrorTol(0); | ||||
|   cg_params.setMaxIterations(100000); | ||||
|   cg_params.setRelativeErrorTol(0); | ||||
|  | @ -377,8 +376,6 @@ TEST(NonlinearOptimizer, Pose2OptimizationWithHuberNoOutlier) { | |||
|   dl_params.setAbsoluteErrorTol(0); | ||||
|   dl_params.setMaxIterations(100); | ||||
| 
 | ||||
|   // cg_params.setVerbosity("ERROR");
 | ||||
| 
 | ||||
|   auto cg_result = NonlinearConjugateGradientOptimizer(fg, init, cg_params).optimize(); | ||||
|   auto gn_result = GaussNewtonOptimizer(fg, init).optimize(); | ||||
|   auto lm_result = LevenbergMarquardtOptimizer(fg, init, lm_params).optimize(); | ||||
|  | @ -414,19 +411,11 @@ TEST(NonlinearOptimizer, Point2LinearOptimizationWithHuber) { | |||
|   expected.insert(1, Point2(1,1.85)); | ||||
| 
 | ||||
|   LevenbergMarquardtParams params; | ||||
|   gtsam::NonlinearConjugateGradientOptimizer::Parameters cg_params; | ||||
|   cg_params.setErrorTol(0); | ||||
|   cg_params.setMaxIterations(100000); | ||||
|   cg_params.setRelativeErrorTol(0); | ||||
|   cg_params.setAbsoluteErrorTol(0); | ||||
|   NonlinearConjugateGradientOptimizer::Parameters cg_params; | ||||
| 
 | ||||
|   // cg_params.setVerbosity("ERROR");
 | ||||
|   auto cg_result = NonlinearConjugateGradientOptimizer(fg, init, cg_params).optimize(); | ||||
| 
 | ||||
|   auto gn_result = GaussNewtonOptimizer(fg, init).optimize(); | ||||
| 
 | ||||
|   auto lm_result = LevenbergMarquardtOptimizer(fg, init, params).optimize(); | ||||
| 
 | ||||
|   auto dl_result = DoglegOptimizer(fg, init).optimize(); | ||||
| 
 | ||||
|   EXPECT(assert_equal(expected, gn_result, 1e-4)); | ||||
|  | @ -468,13 +457,9 @@ TEST(NonlinearOptimizer, Pose2OptimizationWithHuber) { | |||
|   cg_params.setRelativeErrorTol(0); | ||||
|   cg_params.setAbsoluteErrorTol(0); | ||||
| 
 | ||||
|   // cg_params.setVerbosity("ERROR");
 | ||||
|   auto cg_result = NonlinearConjugateGradientOptimizer(fg, init, cg_params).optimize(); | ||||
|   fg.printErrors(cg_result); | ||||
|   auto gn_result = GaussNewtonOptimizer(fg, init).optimize(); | ||||
| 
 | ||||
|   auto lm_result = LevenbergMarquardtOptimizer(fg, init, params).optimize(); | ||||
| 
 | ||||
|   auto dl_result = DoglegOptimizer(fg, init).optimize(); | ||||
| 
 | ||||
|   EXPECT(assert_equal(expected, gn_result, 1e-1)); | ||||
|  | @ -504,7 +489,6 @@ TEST(NonlinearOptimizer, RobustMeanCalculation) { | |||
|   expected.insert(0, 3.33333333); | ||||
| 
 | ||||
|   LevenbergMarquardtParams params; | ||||
|   // params.setVerbosityLM("TRYLAMBDA");
 | ||||
|   params.setAbsoluteErrorTol(1e-20); | ||||
|   params.setRelativeErrorTol(1e-20); | ||||
| 
 | ||||
|  | @ -513,22 +497,12 @@ TEST(NonlinearOptimizer, RobustMeanCalculation) { | |||
|   cg_params.setMaxIterations(10000); | ||||
|   cg_params.setRelativeErrorTol(0); | ||||
|   cg_params.setAbsoluteErrorTol(0); | ||||
|   // cg_params.setVerbosity("ERROR");
 | ||||
| 
 | ||||
|   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(); | ||||
| //  gn_result.print("GN: ");
 | ||||
| //  cout << fg.error(gn_result) << endl << endl << endl;
 | ||||
| 
 | ||||
|   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(); | ||||
| //  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, lm_result, tol)); | ||||
|  |  | |||
		Loading…
	
		Reference in New Issue