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Remove redundant params

release/4.3a0
Fan Jiang 2020-03-03 17:39:28 -05:00
parent 3c0671ba8d
commit 4babfe2491
1 changed files with 1 additions and 38 deletions
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39
tests/testNonlinearOptimizer.cpp
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@ -360,28 +360,11 @@ TEST(NonlinearOptimizer, Pose2OptimizationWithHuberNoOutlier) {
expected.insert(1, Pose2(0.961187, 0.99965, 1.1781)); expected.insert(1, Pose2(0.961187, 0.99965, 1.1781));
LevenbergMarquardtParams lm_params; LevenbergMarquardtParams lm_params;
lm_params.setRelativeErrorTol(0);
lm_params.setAbsoluteErrorTol(0);
lm_params.setMaxIterations(100);
lm_params.setlambdaUpperBound(1e10);
NonlinearConjugateGradientOptimizer::Parameters cg_params;
cg_params.setErrorTol(0);
cg_params.setMaxIterations(100000);
cg_params.setRelativeErrorTol(0);
cg_params.setAbsoluteErrorTol(0);
DoglegParams dl_params;
dl_params.setRelativeErrorTol(0);
dl_params.setAbsoluteErrorTol(0);
dl_params.setMaxIterations(100);
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();
auto dl_result = DoglegOptimizer(fg, init, dl_params).optimize(); auto dl_result = DoglegOptimizer(fg, init).optimize();
EXPECT(assert_equal(expected, cg_result, 3e-2));
EXPECT(assert_equal(expected, gn_result, 3e-2)); EXPECT(assert_equal(expected, gn_result, 3e-2));
EXPECT(assert_equal(expected, lm_result, 3e-2)); EXPECT(assert_equal(expected, lm_result, 3e-2));
EXPECT(assert_equal(expected, dl_result, 3e-2)); EXPECT(assert_equal(expected, dl_result, 3e-2));
@ -411,14 +394,11 @@ TEST(NonlinearOptimizer, Point2LinearOptimizationWithHuber) {
expected.insert(1, Point2(1,1.85)); expected.insert(1, Point2(1,1.85));
LevenbergMarquardtParams params; LevenbergMarquardtParams params;
NonlinearConjugateGradientOptimizer::Parameters cg_params;
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)); EXPECT(assert_equal(expected, gn_result, 1e-4));
EXPECT(assert_equal(expected, lm_result, 1e-4)); EXPECT(assert_equal(expected, lm_result, 1e-4));
EXPECT(assert_equal(expected, dl_result, 1e-4)); EXPECT(assert_equal(expected, dl_result, 1e-4));
@ -451,18 +431,11 @@ TEST(NonlinearOptimizer, Pose2OptimizationWithHuber) {
expected.insert(1, Pose2(0, 10, 1.45212)); expected.insert(1, Pose2(0, 10, 1.45212));
LevenbergMarquardtParams params; LevenbergMarquardtParams params;
gtsam::NonlinearConjugateGradientOptimizer::Parameters cg_params;
cg_params.setErrorTol(0);
cg_params.setMaxIterations(100000);
cg_params.setRelativeErrorTol(0);
cg_params.setAbsoluteErrorTol(0);
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-1));
EXPECT(assert_equal(expected, gn_result, 1e-1)); EXPECT(assert_equal(expected, gn_result, 1e-1));
EXPECT(assert_equal(expected, lm_result, 1e-1)); EXPECT(assert_equal(expected, lm_result, 1e-1));
EXPECT(assert_equal(expected, dl_result, 1e-1)); EXPECT(assert_equal(expected, dl_result, 1e-1));
@ -489,21 +462,11 @@ TEST(NonlinearOptimizer, RobustMeanCalculation) {
expected.insert(0, 3.33333333); expected.insert(0, 3.33333333);
LevenbergMarquardtParams params; LevenbergMarquardtParams params;
params.setAbsoluteErrorTol(1e-20);
params.setRelativeErrorTol(1e-20);
gtsam::NonlinearConjugateGradientOptimizer::Parameters cg_params;
cg_params.setErrorTol(0);
cg_params.setMaxIterations(10000);
cg_params.setRelativeErrorTol(0);
cg_params.setAbsoluteErrorTol(0);
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, 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));
EXPECT(assert_equal(expected, dl_result, tol)); EXPECT(assert_equal(expected, dl_result, tol));