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Add second test

release/4.3a0
Frank Dellaert 2024-09-12 18:32:27 -07:00
parent 2d58a5bac0
commit e59b3afc29
1 changed files with 20 additions and 3 deletions
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23
gtsam/hybrid/tests/testGaussianMixtureFactor.cpp
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@ -555,7 +555,7 @@ TEST(GaussianMixtureFactor, TwoStateModel2) {
using namespace test_two_state_estimation; using namespace test_two_state_estimation;
double mu0 = 1.0, mu1 = 3.0; double mu0 = 1.0, mu1 = 3.0;
double sigma0 = 6.0, sigma1 = 4.0; double sigma0 = 0.5, sigma1 = 2.0;
auto hybridMotionModel = CreateHybridMotionModel(mu0, mu1, sigma0, sigma1); auto hybridMotionModel = CreateHybridMotionModel(mu0, mu1, sigma0, sigma1);
// Start with no measurement on x1, only on x0 // Start with no measurement on x1, only on x0
@ -605,7 +605,7 @@ TEST(GaussianMixtureFactor, TwoStateModel2) {
// Now we add a measurement z1 on x1 // Now we add a measurement z1 on x1
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true); HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
double z1 = 2.2; double z1 = 4.0; // favors m==1
given.insert(Z(1), Vector1(z1)); given.insert(Z(1), Vector1(z1));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given); HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
@ -636,7 +636,24 @@ TEST(GaussianMixtureFactor, TwoStateModel2) {
// Since we have a measurement on z2, we get a definite result // Since we have a measurement on z2, we get a definite result
// Values taken from an importance sampling run with 50k samples: // Values taken from an importance sampling run with 50k samples:
// approximateDiscreteMarginal(hbn, proposalNet, given); // approximateDiscreteMarginal(hbn, proposalNet, given);
DiscreteConditional expected(m1, "0.446345/0.553655"); DiscreteConditional expected(m1, "0.481793/0.518207");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.01));
}
{
// Add a different measurement z1 on that favors m==0
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
double z1 = 1.1;
given.insert_or_assign(Z(1), Vector1(z1));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
// Since we have a measurement on z2, we get a definite result
// Values taken from an importance sampling run with 50k samples:
// approximateDiscreteMarginal(hbn, proposalNet, given);
DiscreteConditional expected(m1, "0.554485/0.445515");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.01)); EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.01));
} }
} }