Less regression, more API tests for s(3).
Frank Dellaert
parent
518ea81d1e
commit
f0770c26cf
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@ -170,14 +170,18 @@ TEST(HybridGaussianFactorGraph, eliminateFullSequentialSimple) {
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// Test API for the smallest switching network.
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// None of these are regression tests.
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TEST(HybridBayesNet, Switching) {
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// Create switching network with two continuous variables and one discrete:
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// ϕ(x0) ϕ(x0,x1,m0) ϕ(x1;z1) ϕ(m0)
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const double betweenSigma = 0.3, priorSigma = 0.1;
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Switching s(2, betweenSigma, priorSigma);
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// Check size of linearized factor graph
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const HybridGaussianFactorGraph& graph = s.linearizedFactorGraph;
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EXPECT_LONGS_EQUAL(4, graph.size());
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// Create some continuous and discrete values
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VectorValues continuousValues{{X(0), Vector1(0.1)}, {X(1), Vector1(1.2)}};
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DiscreteValues modeZero{{M(0), 0}}, modeOne{{M(0), 1}};
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const VectorValues continuousValues{{X(0), Vector1(0.1)}, {X(1), Vector1(1.2)}};
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const DiscreteValues modeZero{{M(0), 0}}, modeOne{{M(0), 1}};
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// Get the hybrid gaussian factor and check it is as expected
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auto hgf = std::dynamic_pointer_cast<HybridGaussianFactor>(graph.at(1));
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@ -195,13 +199,13 @@ TEST(HybridBayesNet, Switching) {
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EXPECT_DOUBLES_EQUAL(betweenModel->negLogConstant(), scalar1, 1e-9);
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// Check error for M(0) = 0
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HybridValues values0{continuousValues, modeZero};
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const HybridValues values0{continuousValues, modeZero};
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double expectedError0 = 0;
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for (const auto& factor : graph) expectedError0 += factor->error(values0);
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EXPECT_DOUBLES_EQUAL(expectedError0, graph.error(values0), 1e-5);
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// Check error for M(0) = 1
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HybridValues values1{continuousValues, modeOne};
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const HybridValues values1{continuousValues, modeOne};
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double expectedError1 = 0;
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for (const auto& factor : graph) expectedError1 += factor->error(values1);
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EXPECT_DOUBLES_EQUAL(expectedError1, graph.error(values1), 1e-5);
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@ -209,22 +213,22 @@ TEST(HybridBayesNet, Switching) {
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// Check errorTree
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AlgebraicDecisionTree<Key> actualErrors = graph.errorTree(continuousValues);
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// Create expected error tree
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AlgebraicDecisionTree<Key> expectedErrors(M(0), expectedError0, expectedError1);
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const AlgebraicDecisionTree<Key> expectedErrors(M(0), expectedError0, expectedError1);
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// Check that the actual error tree matches the expected one
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EXPECT(assert_equal(expectedErrors, actualErrors, 1e-5));
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// Check probPrime
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double probPrime0 = graph.probPrime(values0);
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const double probPrime0 = graph.probPrime(values0);
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EXPECT_DOUBLES_EQUAL(std::exp(-expectedError0), probPrime0, 1e-5);
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double probPrime1 = graph.probPrime(values1);
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const double probPrime1 = graph.probPrime(values1);
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EXPECT_DOUBLES_EQUAL(std::exp(-expectedError1), probPrime1, 1e-5);
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// Check discretePosterior
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AlgebraicDecisionTree<Key> graphPosterior = graph.discretePosterior(continuousValues);
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double sum = probPrime0 + probPrime1;
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AlgebraicDecisionTree<Key> expectedPosterior(M(0), probPrime0 / sum, probPrime1 / sum);
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const AlgebraicDecisionTree<Key> graphPosterior = graph.discretePosterior(continuousValues);
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const double sum = probPrime0 + probPrime1;
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const AlgebraicDecisionTree<Key> expectedPosterior(M(0), probPrime0 / sum, probPrime1 / sum);
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EXPECT(assert_equal(expectedPosterior, graphPosterior, 1e-5));
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// Make the clique of factors connected to x0:
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@ -246,7 +250,7 @@ TEST(HybridBayesNet, Switching) {
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EXPECT_DOUBLES_EQUAL((*hgf)(modeOne).second, actualScalar1, 1e-5);
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// Test eliminate x0
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Ordering ordering{X(0)};
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const Ordering ordering{X(0)};
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auto [conditional, factor] = factors_x0.eliminate(ordering);
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// Check the conditional
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@ -254,6 +258,7 @@ TEST(HybridBayesNet, Switching) {
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EXPECT(conditional->isHybrid());
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auto p_x0_given_x1_m = conditional->asHybrid();
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CHECK(p_x0_given_x1_m);
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EXPECT(HybridGaussianConditional::CheckInvariants(*p_x0_given_x1_m, values1));
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EXPECT_LONGS_EQUAL(1, p_x0_given_x1_m->nrFrontals()); // x0
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EXPECT_LONGS_EQUAL(2, p_x0_given_x1_m->nrParents()); // x1, m0
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@ -270,8 +275,8 @@ TEST(HybridBayesNet, Switching) {
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// Check that the conditional and remaining factor are consistent for both modes
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for (auto&& mode : {modeZero, modeOne}) {
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auto gc = (*p_x0_given_x1_m)(mode);
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auto [gf, scalar] = (*phi_x1_m)(mode);
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const auto gc = (*p_x0_given_x1_m)(mode);
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const auto [gf, scalar] = (*phi_x1_m)(mode);
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// The error of the original factors should equal the sum of errors of the conditional and
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// remaining factor, modulo the normalization constant of the conditional.
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@ -332,12 +337,41 @@ TEST(HybridBayesNet, Switching) {
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}
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// Now test full elimination of the graph:
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auto posterior = graph.eliminateSequential();
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CHECK(posterior);
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auto hybridBayesNet = graph.eliminateSequential();
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CHECK(hybridBayesNet);
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// Check that the posterior P(M|X=continuousValues) from the Bayes net is the same as the
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// same posterior from the graph. This is a sanity check that the elimination is done correctly.
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AlgebraicDecisionTree<Key> bnPosterior = graph.discretePosterior(continuousValues);
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AlgebraicDecisionTree<Key> bnPosterior = hybridBayesNet->discretePosterior(continuousValues);
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EXPECT(assert_equal(graphPosterior, bnPosterior));
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}
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/* ****************************************************************************/
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// Test subset of API for switching network with 3 states.
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// None of these are regression tests.
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TEST(HybridGaussianFactorGraph, ErrorAndProbPrime) {
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// Create switching network with three continuous variables and two discrete:
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// ϕ(x0) ϕ(x0,x1,m0) ϕ(x1,x2,m1) ϕ(x1;z1) ϕ(x2;z2) ϕ(m0) ϕ(m0,m1)
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Switching s(3);
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// Check size of linearized factor graph
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const HybridGaussianFactorGraph& graph = s.linearizedFactorGraph;
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EXPECT_LONGS_EQUAL(7, graph.size());
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// Eliminate the graph
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const HybridBayesNet::shared_ptr hybridBayesNet = graph.eliminateSequential();
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const HybridValues delta = hybridBayesNet->optimize();
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const double error = graph.error(delta);
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// Check that the probability prime is the exponential of the error
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EXPECT(assert_equal(graph.probPrime(delta), exp(-error), 1e-7));
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// Check that the posterior P(M|X=continuousValues) from the Bayes net is the same as the
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// same posterior from the graph. This is a sanity check that the elimination is done correctly.
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const AlgebraicDecisionTree<Key> graphPosterior = graph.discretePosterior(delta.continuous());
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const AlgebraicDecisionTree<Key> bnPosterior =
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hybridBayesNet->discretePosterior(delta.continuous());
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EXPECT(assert_equal(graphPosterior, bnPosterior));
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}
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@ -466,51 +500,6 @@ TEST(HybridGaussianFactorGraph, Conditionals) {
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EXPECT(assert_equal(expected_discrete, result.discrete()));
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}
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/* ****************************************************************************/
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// Test hybrid gaussian factor graph error and unnormalized probabilities
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TEST(HybridGaussianFactorGraph, ErrorAndProbPrime) {
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Switching s(3);
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HybridGaussianFactorGraph graph = s.linearizedFactorGraph;
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HybridBayesNet::shared_ptr hybridBayesNet = graph.eliminateSequential();
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const HybridValues delta = hybridBayesNet->optimize();
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const double error = graph.error(delta);
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// regression
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EXPECT(assert_equal(1.58886, error, 1e-5));
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// Real test:
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EXPECT(assert_equal(graph.probPrime(delta), exp(-error), 1e-7));
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}
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/* ****************************************************************************/
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// Test hybrid gaussian factor graph error and unnormalized probabilities
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TEST(HybridGaussianFactorGraph, ErrorAndProbPrimeTree) {
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// Create switching network with three continuous variables and two discrete:
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// ϕ(x0) ϕ(x0,x1,m0) ϕ(x1,x2,m1) ϕ(x0;z0) ϕ(x1;z1) ϕ(x2;z2) ϕ(m0) ϕ(m0,m1)
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Switching s(3);
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const HybridGaussianFactorGraph& graph = s.linearizedFactorGraph;
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const HybridBayesNet::shared_ptr hybridBayesNet = graph.eliminateSequential();
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const HybridValues delta = hybridBayesNet->optimize();
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// regression test for errorTree
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std::vector<double> leaves = {2.7916153, 1.5888555, 1.7233422, 1.6191947};
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AlgebraicDecisionTree<Key> expectedErrors(s.modes, leaves);
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const auto error_tree = graph.errorTree(delta.continuous());
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EXPECT(assert_equal(expectedErrors, error_tree, 1e-7));
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// regression test for discretePosterior
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const AlgebraicDecisionTree<Key> expectedPosterior(
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s.modes, std::vector{0.095516068, 0.31800092, 0.27798511, 0.3084979});
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auto posterior = graph.discretePosterior(delta.continuous());
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EXPECT(assert_equal(expectedPosterior, posterior, 1e-7));
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}
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/* ****************************************************************************/
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// Test hybrid gaussian factor graph errorTree during incremental operation
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TEST(HybridGaussianFactorGraph, IncrementalErrorTree) {
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@ -528,15 +517,11 @@ TEST(HybridGaussianFactorGraph, IncrementalErrorTree) {
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HybridBayesNet::shared_ptr hybridBayesNet = graph.eliminateSequential();
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EXPECT_LONGS_EQUAL(5, hybridBayesNet->size());
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// Check discrete posterior at optimum
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HybridValues delta = hybridBayesNet->optimize();
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auto error_tree = graph.errorTree(delta.continuous());
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std::vector<DiscreteKey> discrete_keys = {m0, m1};
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std::vector<double> leaves = {2.7916153, 1.5888555, 1.7233422, 1.6191947};
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AlgebraicDecisionTree<Key> expected_error(discrete_keys, leaves);
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// regression
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EXPECT(assert_equal(expected_error, error_tree, 1e-7));
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AlgebraicDecisionTree<Key> graphPosterior = graph.discretePosterior(delta.continuous());
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AlgebraicDecisionTree<Key> bnPosterior = hybridBayesNet->discretePosterior(delta.continuous());
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EXPECT(assert_equal(graphPosterior, bnPosterior));
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graph = HybridGaussianFactorGraph();
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graph.push_back(*hybridBayesNet);
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@ -547,13 +532,9 @@ TEST(HybridGaussianFactorGraph, IncrementalErrorTree) {
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EXPECT_LONGS_EQUAL(7, hybridBayesNet->size());
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delta = hybridBayesNet->optimize();
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auto error_tree2 = graph.errorTree(delta.continuous());
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// regression
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leaves = {
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0.50985198, 0.0097577296, 0.50009425, 0, 0.52922138, 0.029127133, 0.50985105, 0.0097567964};
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AlgebraicDecisionTree<Key> expected_error2(s.modes, leaves);
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EXPECT(assert_equal(expected_error, error_tree, 1e-7));
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graphPosterior = graph.discretePosterior(delta.continuous());
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bnPosterior = hybridBayesNet->discretePosterior(delta.continuous());
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EXPECT(assert_equal(graphPosterior, bnPosterior));
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}
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/* ****************************************************************************/
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