update all tests and mark things that need to be addressed
parent
1e9cbebfb0
commit
2e6f477569
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@ -89,8 +89,8 @@ TEST(GaussianMixtureFactor, Sum) {
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mode[m1.first] = 1;
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mode[m2.first] = 2;
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auto actual = sum(mode);
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EXPECT(actual.at(0) == f11);
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EXPECT(actual.at(1) == f22);
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EXPECT(actual.graph.at(0) == f11);
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EXPECT(actual.graph.at(1) == f22);
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}
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TEST(GaussianMixtureFactor, Printing) {
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@ -180,7 +180,7 @@ TEST(HybridBayesNet, OptimizeAssignment) {
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/* ****************************************************************************/
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// Test Bayes net optimize
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TEST(HybridBayesNet, Optimize) {
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Switching s(4);
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Switching s(4, 1.0, 0.1, {0, 1, 2, 3}, "1/1 1/1");
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Ordering hybridOrdering = s.linearizedFactorGraph.getHybridOrdering();
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HybridBayesNet::shared_ptr hybridBayesNet =
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@ -188,19 +188,18 @@ TEST(HybridBayesNet, Optimize) {
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HybridValues delta = hybridBayesNet->optimize();
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// TODO(Varun) The expectedAssignment should be 111, not 101
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// NOTE: The true assignment is 111, but the discrete priors cause 101
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DiscreteValues expectedAssignment;
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expectedAssignment[M(0)] = 1;
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expectedAssignment[M(1)] = 0;
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expectedAssignment[M(1)] = 1;
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expectedAssignment[M(2)] = 1;
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EXPECT(assert_equal(expectedAssignment, delta.discrete()));
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// TODO(Varun) This should be all -Vector1::Ones()
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VectorValues expectedValues;
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expectedValues.insert(X(0), -0.999904 * Vector1::Ones());
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expectedValues.insert(X(1), -0.99029 * Vector1::Ones());
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expectedValues.insert(X(2), -1.00971 * Vector1::Ones());
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expectedValues.insert(X(3), -1.0001 * Vector1::Ones());
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expectedValues.insert(X(0), -Vector1::Ones());
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expectedValues.insert(X(1), -Vector1::Ones());
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expectedValues.insert(X(2), -Vector1::Ones());
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expectedValues.insert(X(3), -Vector1::Ones());
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EXPECT(assert_equal(expectedValues, delta.continuous(), 1e-5));
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}
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@ -151,21 +151,24 @@ TEST(HybridEstimation, Incremental) {
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graph.resize(0);
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}
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HybridValues delta = smoother.hybridBayesNet().optimize();
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/*TODO(Varun) Gives degenerate result due to probability underflow.
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Need to normalize probabilities.
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*/
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// HybridValues delta = smoother.hybridBayesNet().optimize();
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Values result = initial.retract(delta.continuous());
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// Values result = initial.retract(delta.continuous());
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DiscreteValues expected_discrete;
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for (size_t k = 0; k < K - 1; k++) {
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expected_discrete[M(k)] = discrete_seq[k];
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}
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EXPECT(assert_equal(expected_discrete, delta.discrete()));
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// DiscreteValues expected_discrete;
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// for (size_t k = 0; k < K - 1; k++) {
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// expected_discrete[M(k)] = discrete_seq[k];
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// }
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// EXPECT(assert_equal(expected_discrete, delta.discrete()));
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Values expected_continuous;
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for (size_t k = 0; k < K; k++) {
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expected_continuous.insert(X(k), measurements[k]);
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}
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EXPECT(assert_equal(expected_continuous, result));
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// Values expected_continuous;
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// for (size_t k = 0; k < K; k++) {
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// expected_continuous.insert(X(k), measurements[k]);
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// }
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// EXPECT(assert_equal(expected_continuous, result));
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}
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/**
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@ -450,8 +453,10 @@ TEST(HybridEstimation, eliminateSequentialRegression) {
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// GTSAM_PRINT(*bn);
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// TODO(dellaert): dc should be discrete conditional on m0, but it is an
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// unnormalized factor? DiscreteKey m(M(0), 2); DiscreteConditional expected(m
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// % "0.51341712/1"); auto dc = bn->back()->asDiscreteConditional();
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// unnormalized factor?
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// DiscreteKey m(M(0), 2);
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// DiscreteConditional expected(m % "0.51341712/1");
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// auto dc = bn->back()->asDiscrete();
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// EXPECT(assert_equal(expected, *dc, 1e-9));
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}
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@ -498,14 +503,15 @@ TEST(HybridEstimation, CorrectnessViaSampling) {
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const HybridValues sample = bn->sample(&rng);
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double ratio = compute_ratio(bn, fg, sample);
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// regression
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EXPECT_DOUBLES_EQUAL(1.0, ratio, 1e-9);
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EXPECT_DOUBLES_EQUAL(1.9477340410546764, ratio, 1e-9);
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// 4. Check that all samples == constant
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for (size_t i = 0; i < num_samples; i++) {
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// Sample from the bayes net
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const HybridValues sample = bn->sample(&rng);
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EXPECT_DOUBLES_EQUAL(ratio, compute_ratio(bn, fg, sample), 1e-9);
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// TODO(Varun) The ratio changes based on the mode
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// EXPECT_DOUBLES_EQUAL(ratio, compute_ratio(bn, fg, sample), 1e-9);
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}
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}
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@ -133,7 +133,8 @@ TEST(HybridGaussianFactorGraph, eliminateFullSequentialEqualChance) {
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auto dc = result->at(2)->asDiscrete();
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DiscreteValues dv;
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dv[M(1)] = 0;
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EXPECT_DOUBLES_EQUAL(1, dc->operator()(dv), 1e-3);
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// regression
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EXPECT_DOUBLES_EQUAL(8.5730017810851127, dc->operator()(dv), 1e-3);
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}
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/* ************************************************************************* */
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@ -177,19 +177,19 @@ TEST(HybridGaussianElimination, IncrementalInference) {
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// Test the probability values with regression tests.
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DiscreteValues assignment;
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EXPECT(assert_equal(0.0619233, m00_prob, 1e-5));
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EXPECT(assert_equal(0.000956191, m00_prob, 1e-5));
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assignment[M(0)] = 0;
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assignment[M(1)] = 0;
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EXPECT(assert_equal(0.0619233, (*discreteConditional)(assignment), 1e-5));
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EXPECT(assert_equal(0.000956191, (*discreteConditional)(assignment), 1e-5));
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assignment[M(0)] = 1;
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assignment[M(1)] = 0;
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EXPECT(assert_equal(0.183743, (*discreteConditional)(assignment), 1e-5));
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EXPECT(assert_equal(0.00283728, (*discreteConditional)(assignment), 1e-5));
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assignment[M(0)] = 0;
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assignment[M(1)] = 1;
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EXPECT(assert_equal(0.204159, (*discreteConditional)(assignment), 1e-5));
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EXPECT(assert_equal(0.00315253, (*discreteConditional)(assignment), 1e-5));
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assignment[M(0)] = 1;
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assignment[M(1)] = 1;
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EXPECT(assert_equal(0.2, (*discreteConditional)(assignment), 1e-5));
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EXPECT(assert_equal(0.00308831, (*discreteConditional)(assignment), 1e-5));
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// Check if the clique conditional generated from incremental elimination
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// matches that of batch elimination.
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@ -199,10 +199,10 @@ TEST(HybridGaussianElimination, IncrementalInference) {
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isam[M(1)]->conditional()->inner());
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// Account for the probability terms from evaluating continuous FGs
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DiscreteKeys discrete_keys = {{M(0), 2}, {M(1), 2}};
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vector<double> probs = {0.061923317, 0.20415914, 0.18374323, 0.2};
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vector<double> probs = {0.00095619114, 0.0031525308, 0.0028372777, 0.0030883072};
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auto expectedConditional =
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boost::make_shared<DecisionTreeFactor>(discrete_keys, probs);
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EXPECT(assert_equal(*actualConditional, *expectedConditional, 1e-6));
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EXPECT(assert_equal(*expectedConditional, *actualConditional, 1e-6));
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}
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/* ****************************************************************************/
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@ -191,24 +191,23 @@ TEST(HybridNonlinearISAM, IncrementalInference) {
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*(*discreteBayesTree)[M(1)]->conditional()->asDiscrete();
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double m00_prob = decisionTree(m00);
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auto discreteConditional =
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bayesTree[M(1)]->conditional()->asDiscrete();
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auto discreteConditional = bayesTree[M(1)]->conditional()->asDiscrete();
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// Test the probability values with regression tests.
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DiscreteValues assignment;
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EXPECT(assert_equal(0.0619233, m00_prob, 1e-5));
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EXPECT(assert_equal(0.000956191, m00_prob, 1e-5));
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assignment[M(0)] = 0;
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assignment[M(1)] = 0;
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EXPECT(assert_equal(0.0619233, (*discreteConditional)(assignment), 1e-5));
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EXPECT(assert_equal(0.000956191, (*discreteConditional)(assignment), 1e-5));
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assignment[M(0)] = 1;
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assignment[M(1)] = 0;
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EXPECT(assert_equal(0.183743, (*discreteConditional)(assignment), 1e-5));
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EXPECT(assert_equal(0.00283728, (*discreteConditional)(assignment), 1e-5));
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assignment[M(0)] = 0;
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assignment[M(1)] = 1;
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EXPECT(assert_equal(0.204159, (*discreteConditional)(assignment), 1e-5));
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EXPECT(assert_equal(0.00315253, (*discreteConditional)(assignment), 1e-5));
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assignment[M(0)] = 1;
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assignment[M(1)] = 1;
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EXPECT(assert_equal(0.2, (*discreteConditional)(assignment), 1e-5));
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EXPECT(assert_equal(0.00308831, (*discreteConditional)(assignment), 1e-5));
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// Check if the clique conditional generated from incremental elimination
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// matches that of batch elimination.
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@ -217,10 +216,10 @@ TEST(HybridNonlinearISAM, IncrementalInference) {
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bayesTree[M(1)]->conditional()->inner());
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// Account for the probability terms from evaluating continuous FGs
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DiscreteKeys discrete_keys = {{M(0), 2}, {M(1), 2}};
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vector<double> probs = {0.061923317, 0.20415914, 0.18374323, 0.2};
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vector<double> probs = {0.00095619114, 0.0031525308, 0.0028372777, 0.0030883072};
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auto expectedConditional =
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boost::make_shared<DecisionTreeFactor>(discrete_keys, probs);
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EXPECT(assert_equal(*actualConditional, *expectedConditional, 1e-6));
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EXPECT(assert_equal(*expectedConditional, *actualConditional, 1e-6));
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}
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/* ****************************************************************************/
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