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Merge pull request #1394 from borglab/hybrid/pruning_test

release/4.3a0
Frank Dellaert 2023-01-19 09:27:44 -08:00 committed by GitHub
parent 3460147df8 b5d574552c
commit d920d94198
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GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 12 additions and 14 deletions
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26
gtsam/hybrid/tests/testHybridBayesNet.cpp
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@ -220,7 +220,7 @@ TEST(HybridBayesNet, Optimize) {
/* ****************************************************************************/ /* ****************************************************************************/
// Test Bayes net error // Test Bayes net error
TEST(HybridBayesNet, logProbability) { TEST(HybridBayesNet, Pruning) {
Switching s(3); Switching s(3);
HybridBayesNet::shared_ptr posterior = HybridBayesNet::shared_ptr posterior =
@ -228,25 +228,22 @@ TEST(HybridBayesNet, logProbability) {
EXPECT_LONGS_EQUAL(5, posterior->size()); EXPECT_LONGS_EQUAL(5, posterior->size());
HybridValues delta = posterior->optimize(); HybridValues delta = posterior->optimize();
auto actualTree = posterior->logProbability(delta.continuous()); auto actualTree = posterior->evaluate(delta.continuous());
// Regression test on density tree.
std::vector<DiscreteKey> discrete_keys = {{M(0), 2}, {M(1), 2}}; std::vector<DiscreteKey> discrete_keys = {{M(0), 2}, {M(1), 2}};
std::vector<double> leaves = {1.8101301, 3.0128899, 2.8784032, 2.9825507}; std::vector<double> leaves = {6.1112424, 20.346113, 17.785849, 19.738098};
AlgebraicDecisionTree<Key> expected(discrete_keys, leaves); AlgebraicDecisionTree<Key> expected(discrete_keys, leaves);
// regression
EXPECT(assert_equal(expected, actualTree, 1e-6)); EXPECT(assert_equal(expected, actualTree, 1e-6));
// logProbability on pruned Bayes net // Prune and get probabilities
auto prunedBayesNet = posterior->prune(2); auto prunedBayesNet = posterior->prune(2);
auto prunedTree = prunedBayesNet.logProbability(delta.continuous()); auto prunedTree = prunedBayesNet.evaluate(delta.continuous());
std::vector<double> pruned_leaves = {2e50, 3.0128899, 2e50, 2.9825507}; // Regression test on pruned logProbability tree
std::vector<double> pruned_leaves = {0.0, 20.346113, 0.0, 19.738098};
AlgebraicDecisionTree<Key> expected_pruned(discrete_keys, pruned_leaves); AlgebraicDecisionTree<Key> expected_pruned(discrete_keys, pruned_leaves);
EXPECT(assert_equal(expected_pruned, prunedTree, 1e-6));
// regression
// TODO(dellaert): fix pruning, I have no insight in this code.
// EXPECT(assert_equal(expected_pruned, prunedTree, 1e-6));
// Verify logProbability computation and check specific logProbability value // Verify logProbability computation and check specific logProbability value
const DiscreteValues discrete_values{{M(0), 1}, {M(1), 1}}; const DiscreteValues discrete_values{{M(0), 1}, {M(1), 1}};
@ -261,8 +258,9 @@ TEST(HybridBayesNet, logProbability) {
logProbability += logProbability +=
posterior->at(4)->asDiscrete()->logProbability(hybridValues); posterior->at(4)->asDiscrete()->logProbability(hybridValues);
EXPECT_DOUBLES_EQUAL(logProbability, actualTree(discrete_values), 1e-9); double density = exp(logProbability);
EXPECT_DOUBLES_EQUAL(logProbability, prunedTree(discrete_values), 1e-9); EXPECT_DOUBLES_EQUAL(density, actualTree(discrete_values), 1e-9);
EXPECT_DOUBLES_EQUAL(density, prunedTree(discrete_values), 1e-9);
EXPECT_DOUBLES_EQUAL(logProbability, posterior->logProbability(hybridValues), EXPECT_DOUBLES_EQUAL(logProbability, posterior->logProbability(hybridValues),
1e-9); 1e-9);
} }