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fix testHybridNonlinearISAM

release/4.3a0
Varun Agrawal 2025-01-03 11:37:43 -05:00
parent b9293b4e58
commit 8e36361e52
1 changed files with 5 additions and 8 deletions
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13
gtsam/hybrid/tests/testHybridNonlinearISAM.cpp
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@ -265,16 +265,12 @@ TEST(HybridNonlinearISAM, 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>(
bayesTree[M(0)]->conditional()->inner()); bayesTree[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.
@ -520,12 +516,13 @@ TEST(HybridNonlinearISAM, NonTrivial) {
// The final discrete graph should not be empty since we have eliminated // The final discrete graph should not be empty since we have eliminated
// all continuous variables. // all continuous variables.
auto discreteTree = bayesTree[M(3)]->conditional()->asDiscrete(); auto discreteTree =
bayesTree[M(3)]->conditional()->asDiscrete<DiscreteTableConditional>();
EXPECT_LONGS_EQUAL(3, discreteTree->size()); EXPECT_LONGS_EQUAL(3, discreteTree->size());
// 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); discreteGraph.push_back(discreteTree->toDecisionTreeFactor());
DiscreteValues optimal_assignment = discreteGraph.optimize(); DiscreteValues optimal_assignment = discreteGraph.optimize();
DiscreteValues expected_assignment; DiscreteValues expected_assignment;