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f(x0, x1, m0; z0, z1) now has constant ratios !

release/4.3a0
Frank Dellaert 2023-01-02 18:36:50 -05:00
parent c3f04695b0
commit f726cf6571
1 changed files with 4 additions and 6 deletions
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10
gtsam/hybrid/tests/testHybridEstimation.cpp
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@ -485,14 +485,14 @@ TEST(HybridEstimation, CorrectnessViaSampling) {
// 2. Eliminate into BN
const Ordering ordering = fg->getHybridOrdering();
HybridBayesNet::shared_ptr bn = fg->eliminateSequential(ordering);
const HybridBayesNet::shared_ptr bn = fg->eliminateSequential(ordering);
// Set up sampling
std::mt19937_64 rng(11);
// Compute the log-ratio between the Bayes net and the factor graph.
auto compute_ratio = [&](const HybridValues& sample) -> double {
return bn->error(sample) - fg->error(sample);
return bn->evaluate(sample) / fg->probPrime(sample);
};
// The error evaluated by the factor graph and the Bayes net should differ by
@ -500,7 +500,7 @@ TEST(HybridEstimation, CorrectnessViaSampling) {
const HybridValues sample = bn->sample(&rng);
double expected_ratio = compute_ratio(sample);
// regression
EXPECT_DOUBLES_EQUAL(1.9477340410546764, expected_ratio, 1e-9);
EXPECT_DOUBLES_EQUAL(0.728588, expected_ratio, 1e-6);
// 3. Do sampling
constexpr int num_samples = 10;
@ -509,9 +509,7 @@ TEST(HybridEstimation, CorrectnessViaSampling) {
const HybridValues sample = bn->sample(&rng);
// 4. Check that the ratio is constant.
// TODO(Varun) The ratio changes based on the mode
// std::cout << compute_ratio(sample) << std::endl;
// EXPECT_DOUBLES_EQUAL(expected_ratio, compute_ratio(sample), 1e-9);
EXPECT_DOUBLES_EQUAL(expected_ratio, compute_ratio(sample), 1e-6);
}
}