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refactor tests a bit

release/4.3a0
Varun Agrawal 2022-12-22 21:01:43 +05:30
parent b68530dcbb
commit 8272854378
1 changed files with 17 additions and 8 deletions
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25
gtsam/hybrid/tests/testHybridEstimation.cpp
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@ -92,7 +92,6 @@ TEST(HybridEstimation, Full) {
HybridBayesNet::shared_ptr bayesNet = HybridBayesNet::shared_ptr bayesNet =
graph.eliminateSequential(hybridOrdering); graph.eliminateSequential(hybridOrdering);
bayesNet->print();
EXPECT_LONGS_EQUAL(2 * K - 1, bayesNet->size()); EXPECT_LONGS_EQUAL(2 * K - 1, bayesNet->size());
HybridValues delta = bayesNet->optimize(); HybridValues delta = bayesNet->optimize();
@ -315,10 +314,23 @@ TEST(HybridEstimation, Probability) {
Switching switching(K, between_sigma, measurement_sigma, measurements, Switching switching(K, between_sigma, measurement_sigma, measurements,
"1/1 1/1"); "1/1 1/1");
auto graph = switching.linearizedFactorGraph; auto graph = switching.linearizedFactorGraph;
Ordering ordering = getOrdering(graph, HybridGaussianFactorGraph());
HybridBayesNet::shared_ptr bayesNet = graph.eliminateSequential(ordering); // Continuous elimination
auto discreteConditional = bayesNet->atDiscrete(bayesNet->size() - 3); Ordering continuous_ordering(graph.continuousKeys());
HybridBayesNet::shared_ptr bayesNet;
HybridGaussianFactorGraph::shared_ptr discreteGraph;
std::tie(bayesNet, discreteGraph) =
graph.eliminatePartialSequential(continuous_ordering);
// Discrete elimination
Ordering discrete_ordering(graph.discreteKeys());
auto discreteBayesNet = discreteGraph->eliminateSequential(discrete_ordering);
// Add the discrete conditionals to make it a full bayes net.
for (auto discrete_conditional : *discreteBayesNet) {
bayesNet->add(discrete_conditional);
}
auto discreteConditional = discreteBayesNet->atDiscrete(0);
// Test if the probPrimeTree matches the probability of // Test if the probPrimeTree matches the probability of
// the individual factor graphs // the individual factor graphs
@ -413,11 +425,8 @@ TEST(HybridEstimation, ProbabilityMultifrontal) {
probPrimeTree(discrete_assignment), 1e-8); probPrimeTree(discrete_assignment), 1e-8);
} }
discreteGraph->add(DecisionTreeFactor(discrete_keys, probPrimeTree));
Ordering discrete(graph.discreteKeys()); Ordering discrete(graph.discreteKeys());
auto discreteBayesTree = auto discreteBayesTree = discreteGraph->eliminateMultifrontal(discrete);
discreteGraph->BaseEliminateable::eliminateMultifrontal(discrete);
EXPECT_LONGS_EQUAL(1, discreteBayesTree->size()); EXPECT_LONGS_EQUAL(1, discreteBayesTree->size());
// DiscreteBayesTree should have only 1 clique // DiscreteBayesTree should have only 1 clique