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fix testHybridGaussianISAM

release/4.3a0
Varun Agrawal 2025-01-03 11:37:31 -05:00
parent 446263cb12
commit b9293b4e58
1 changed files with 7 additions and 8 deletions
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15
gtsam/hybrid/tests/testHybridGaussianISAM.cpp
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@ -141,7 +141,8 @@ TEST(HybridGaussianISAM, IncrementalInference) {
expectedRemainingGraph->eliminateMultifrontal(discreteOrdering); expectedRemainingGraph->eliminateMultifrontal(discreteOrdering);
// Test the probability values with regression tests. // Test the probability values with regression tests.
auto discrete = isam[M(1)]->conditional()->asDiscrete(); auto discrete =
isam[M(1)]->conditional()->asDiscrete<DiscreteTableConditional>();
EXPECT(assert_equal(0.095292, (*discrete)({{M(0), 0}, {M(1), 0}}), 1e-5)); EXPECT(assert_equal(0.095292, (*discrete)({{M(0), 0}, {M(1), 0}}), 1e-5));
EXPECT(assert_equal(0.282758, (*discrete)({{M(0), 1}, {M(1), 0}}), 1e-5)); EXPECT(assert_equal(0.282758, (*discrete)({{M(0), 1}, {M(1), 0}}), 1e-5));
EXPECT(assert_equal(0.314175, (*discrete)({{M(0), 0}, {M(1), 1}}), 1e-5)); EXPECT(assert_equal(0.314175, (*discrete)({{M(0), 0}, {M(1), 1}}), 1e-5));
@ -221,16 +222,12 @@ TEST(HybridGaussianISAM, ApproxInference) {
1 1 1 Leaf 0.5 1 1 1 Leaf 0.5
*/ */
auto discreteConditional_m0 = *dynamic_pointer_cast<DiscreteConditional>( auto discreteConditional_m0 = *dynamic_pointer_cast<DiscreteTableConditional>(
incrementalHybrid[M(0)]->conditional()->inner()); incrementalHybrid[M(0)]->conditional()->inner());
EXPECT(discreteConditional_m0.keys() == KeyVector({M(0), M(1), M(2)})); EXPECT(discreteConditional_m0.keys() == KeyVector({M(0), M(1), M(2)}));
// Get the number of elements which are greater than 0.
auto count = [](const double &value, int count) {
return value > 0 ? count + 1 : count;
};
// Check that the number of leaves after pruning is 5. // Check that the number of leaves after pruning is 5.
EXPECT_LONGS_EQUAL(5, discreteConditional_m0.fold(count, 0)); EXPECT_LONGS_EQUAL(5, discreteConditional_m0.nrValues());
// Check that the hybrid nodes of the bayes net match those of the pre-pruning // Check that the hybrid nodes of the bayes net match those of the pre-pruning
// bayes net, at the same positions. // bayes net, at the same positions.
@ -477,7 +474,9 @@ TEST(HybridGaussianISAM, NonTrivial) {
// Test if the optimal discrete mode assignment is (1, 1, 1). // Test if the optimal discrete mode assignment is (1, 1, 1).
DiscreteFactorGraph discreteGraph; DiscreteFactorGraph discreteGraph;
discreteGraph.push_back(discreteTree); // discreteTree is a DiscreteTableConditional, so we convert to
// DecisionTreeFactor for the DiscreteFactorGraph
discreteGraph.push_back(discreteTree->toDecisionTreeFactor());
DiscreteValues optimal_assignment = discreteGraph.optimize(); DiscreteValues optimal_assignment = discreteGraph.optimize();
DiscreteValues expected_assignment; DiscreteValues expected_assignment;