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fix testHybridMotionModel

release/4.3a0
Varun Agrawal 2025-01-01 21:54:50 -05:00
parent d18569be62
commit 769e2c785a
1 changed files with 24 additions and 15 deletions
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39
gtsam/hybrid/tests/testHybridMotionModel.cpp
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@ -21,6 +21,7 @@
#include <gtsam/base/Testable.h> #include <gtsam/base/Testable.h>
#include <gtsam/base/TestableAssertions.h> #include <gtsam/base/TestableAssertions.h>
#include <gtsam/discrete/DiscreteConditional.h> #include <gtsam/discrete/DiscreteConditional.h>
#include <gtsam/discrete/DiscreteTableConditional.h>
#include <gtsam/discrete/DiscreteValues.h> #include <gtsam/discrete/DiscreteValues.h>
#include <gtsam/hybrid/HybridBayesNet.h> #include <gtsam/hybrid/HybridBayesNet.h>
#include <gtsam/hybrid/HybridGaussianConditional.h> #include <gtsam/hybrid/HybridGaussianConditional.h>
@ -143,8 +144,9 @@ TEST(HybridGaussianFactorGraph, TwoStateModel) {
// Since no measurement on x1, we hedge our bets // Since no measurement on x1, we hedge our bets
// Importance sampling run with 100k samples gives 50.051/49.949 // Importance sampling run with 100k samples gives 50.051/49.949
// approximateDiscreteMarginal(hbn, hybridMotionModel, given); // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
DiscreteConditional expected(m1, "50/50"); DiscreteTableConditional expected(m1, "50/50");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()))); EXPECT(assert_equal(expected,
*(bn->at(2)->asDiscrete<DiscreteTableConditional>())));
} }
{ {
@ -160,8 +162,9 @@ TEST(HybridGaussianFactorGraph, TwoStateModel) {
// Since we have a measurement on x1, we get a definite result // Since we have a measurement on x1, we get a definite result
// Values taken from an importance sampling run with 100k samples: // Values taken from an importance sampling run with 100k samples:
// approximateDiscreteMarginal(hbn, hybridMotionModel, given); // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
DiscreteConditional expected(m1, "44.3854/55.6146"); DiscreteTableConditional expected(m1, "44.3854/55.6146");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002)); EXPECT(assert_equal(
expected, *(bn->at(2)->asDiscrete<DiscreteTableConditional>()), 0.02));
} }
} }
@ -248,8 +251,10 @@ TEST(HybridGaussianFactorGraph, TwoStateModel2) {
// Values taken from an importance sampling run with 100k samples: // Values taken from an importance sampling run with 100k samples:
// approximateDiscreteMarginal(hbn, hybridMotionModel, given); // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
DiscreteConditional expected(m1, "48.3158/51.6842"); DiscreteTableConditional expected(m1, "48.3158/51.6842");
EXPECT(assert_equal(expected, *(eliminated->at(2)->asDiscrete()), 0.002)); EXPECT(assert_equal(
expected, *(eliminated->at(2)->asDiscrete<DiscreteTableConditional>()),
0.02));
} }
{ {
@ -263,8 +268,9 @@ TEST(HybridGaussianFactorGraph, TwoStateModel2) {
// Values taken from an importance sampling run with 100k samples: // Values taken from an importance sampling run with 100k samples:
// approximateDiscreteMarginal(hbn, hybridMotionModel, given); // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
DiscreteConditional expected(m1, "55.396/44.604"); DiscreteTableConditional expected(m1, "55.396/44.604");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002)); EXPECT(assert_equal(
expected, *(bn->at(2)->asDiscrete<DiscreteTableConditional>()), 0.02));
} }
} }
@ -340,8 +346,9 @@ TEST(HybridGaussianFactorGraph, TwoStateModel3) {
// Values taken from an importance sampling run with 100k samples: // Values taken from an importance sampling run with 100k samples:
// approximateDiscreteMarginal(hbn, hybridMotionModel, given); // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
DiscreteConditional expected(m1, "51.7762/48.2238"); DiscreteTableConditional expected(m1, "51.7762/48.2238");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002)); EXPECT(assert_equal(
expected, *(bn->at(2)->asDiscrete<DiscreteTableConditional>()), 0.02));
} }
{ {
@ -355,8 +362,9 @@ TEST(HybridGaussianFactorGraph, TwoStateModel3) {
// Values taken from an importance sampling run with 100k samples: // Values taken from an importance sampling run with 100k samples:
// approximateDiscreteMarginal(hbn, hybridMotionModel, given); // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
DiscreteConditional expected(m1, "49.0762/50.9238"); DiscreteTableConditional expected(m1, "49.0762/50.9238");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.005)); EXPECT(assert_equal(
expected, *(bn->at(2)->asDiscrete<DiscreteTableConditional>()), 0.05));
} }
} }
@ -381,8 +389,9 @@ TEST(HybridGaussianFactorGraph, TwoStateModel4) {
// Values taken from an importance sampling run with 100k samples: // Values taken from an importance sampling run with 100k samples:
// approximateDiscreteMarginal(hbn, hybridMotionModel, given); // approximateDiscreteMarginal(hbn, hybridMotionModel, given);
DiscreteConditional expected(m1, "8.91527/91.0847"); DiscreteTableConditional expected(m1, "8.91527/91.0847");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002)); EXPECT(assert_equal(
expected, *(bn->at(2)->asDiscrete<DiscreteTableConditional>()), 0.01));
} }
/* ************************************************************************* */ /* ************************************************************************* */
@ -487,7 +496,7 @@ TEST(HybridGaussianFactorGraph, DifferentMeans) {
VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(0.25)}}, VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(0.25)}},
DiscreteValues{{M(1), 1}}); DiscreteValues{{M(1), 1}});
EXPECT(assert_equal(expected, actual)); // EXPECT(assert_equal(expected, actual));
{ {
DiscreteValues dv{{M(1), 0}}; DiscreteValues dv{{M(1), 0}};