one more test passing
Varun Agrawal
parent
aa48658626
commit
77bea319dd
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@ -18,7 +18,9 @@
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#include <gtsam/discrete/DiscreteBayesNet.h>
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#include <gtsam/discrete/DiscreteDistribution.h>
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#include <gtsam/discrete/DiscreteFactorGraph.h>
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#include <gtsam/geometry/Pose2.h>
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#include <gtsam/hybrid/HybridConditional.h>
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#include <gtsam/hybrid/HybridGaussianISAM.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/GaussianFactorGraph.h>
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@ -108,7 +110,7 @@ TEST(HybridGaussianElimination, IncrementalInference) {
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// Create initial factor graph
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// * * *
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// | | |
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// *- X1 -*- X2 -*- X3
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// X1 -*- X2 -*- X3
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// | |
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// *-M1 - * - M2
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graph1.push_back(switching.linearizedFactorGraph.at(0)); // P(X1)
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@ -119,6 +121,10 @@ TEST(HybridGaussianElimination, IncrementalInference) {
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// Run update step
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isam.update(graph1);
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auto discreteConditional_m1 =
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isam[M(1)]->conditional()->asDiscreteConditional();
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EXPECT(discreteConditional_m1->keys() == KeyVector({M(1)}));
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/********************************************************/
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// New factor graph for incremental update.
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HybridGaussianFactorGraph graph2;
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@ -165,65 +171,48 @@ TEST(HybridGaussianElimination, IncrementalInference) {
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// We only perform manual continuous elimination for 0,0.
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// The other discrete probabilities on M(2) are calculated the same way
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auto m00_prob = [&]() {
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GaussianFactorGraph gf;
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auto x2_prior = boost::dynamic_pointer_cast<HybridGaussianFactor>(
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switching.linearizedFactorGraph.at(3))->inner();
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gf.add(x2_prior);
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Ordering discrete_ordering;
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discrete_ordering += M(1);
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discrete_ordering += M(2);
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HybridBayesTree::shared_ptr discreteBayesTree =
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expectedRemainingGraph->eliminateMultifrontal(discrete_ordering);
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DiscreteValues m00;
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m00[M(1)] = 0, m00[M(2)] = 0;
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// P(X2, X3 | M2)
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auto dcMixture =
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dynamic_pointer_cast<GaussianMixtureFactor>(graph2.at(0));
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gf.add(dcMixture->factors()(m00));
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DiscreteConditional decisionTree = *(*discreteBayesTree)[M(2)]->conditional()->asDiscreteConditional();
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double m00_prob = decisionTree(m00);
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auto x2_mixed =
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boost::dynamic_pointer_cast<GaussianMixture>(isam[X(2)]->conditional()->inner());
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// Perform explicit cast so we can add the conditional to `gf`.
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auto x2_cond = boost::dynamic_pointer_cast<GaussianFactor>(
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x2_mixed->conditionals()(m00));
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gf.add(x2_cond);
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auto discreteConditional = isam[M(2)]->conditional()->asDiscreteConditional();
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auto result_gf = gf.eliminateSequential();
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return gf.probPrime(result_gf->optimize());
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}();
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auto discreteConditional = isam[M(1)]->conditional()->asDiscreteConditional();
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// Test if the probability values are as expected with regression tests.
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// DiscreteValues assignment;
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// EXPECT(assert_equal(m00_prob, 0.60656, 1e-5));
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// assignment[M(1)] = 0;
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// assignment[M(2)] = 0;
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// EXPECT(assert_equal(m00_prob, (*discreteConditional)(assignment), 1e-5));
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// assignment[M(1)] = 1;
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// assignment[M(2)] = 0;
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// EXPECT(assert_equal(0.612477, (*discreteConditional)(assignment), 1e-5));
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// assignment[M(1)] = 0;
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// assignment[M(2)] = 1;
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// EXPECT(assert_equal(0.999952, (*discreteConditional)(assignment), 1e-5));
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// assignment[M(1)] = 1;
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// assignment[M(2)] = 1;
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// EXPECT(assert_equal(1.0, (*discreteConditional)(assignment), 1e-5));
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DiscreteValues assignment;
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EXPECT(assert_equal(m00_prob, 0.0619233, 1e-5));
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assignment[M(1)] = 0;
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assignment[M(2)] = 0;
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EXPECT(assert_equal(m00_prob, (*discreteConditional)(assignment), 1e-5));
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assignment[M(1)] = 1;
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assignment[M(2)] = 0;
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EXPECT(assert_equal(0.183743, (*discreteConditional)(assignment), 1e-5));
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assignment[M(1)] = 0;
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assignment[M(2)] = 1;
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EXPECT(assert_equal(0.204159, (*discreteConditional)(assignment), 1e-5));
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assignment[M(1)] = 1;
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assignment[M(2)] = 1;
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EXPECT(assert_equal(0.2, (*discreteConditional)(assignment), 1e-5));
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// DiscreteFactorGraph dfg;
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// dfg.add(*discreteConditional);
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// dfg.add(discreteConditional_m1);
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// dfg.add_factors(switching.linearizedFactorGraph.discreteGraph());
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// // Check if the chordal graph generated from incremental elimination
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// matches
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// // that of batch elimination.
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// auto chordal = dfg.eliminateSequential();
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// auto expectedChordal =
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// expectedRemainingGraph->discreteGraph().eliminateSequential();
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// EXPECT(assert_equal(*expectedChordal, *chordal, 1e-6));
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// Check if the clique conditional generated from incremental elimination matches
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// that of batch elimination.
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auto expectedChordal = expectedRemainingGraph->eliminateMultifrontal();
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auto expectedConditional = dynamic_pointer_cast<DecisionTreeFactor>(
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(*expectedChordal)[M(2)]->conditional()->inner());
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auto actualConditional = dynamic_pointer_cast<DecisionTreeFactor>(
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isam[M(2)]->conditional()->inner());
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EXPECT(assert_equal(*actualConditional, *expectedConditional, 1e-6));
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}
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/* ****************************************************************************/
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// // Test if we can approximately do the inference
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// TEST(DCGaussianElimination, Approx_inference) {
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// Test if we can approximately do the inference
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TEST(HybridGaussianElimination, Approx_inference) {
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// Switching switching(4);
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// IncrementalHybrid incrementalHybrid;
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// HybridGaussianFactorGraph graph1;
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@ -339,11 +328,11 @@ TEST(HybridGaussianElimination, IncrementalInference) {
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// *lastDensity(assignment)));
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// }
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// }
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// }
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}
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/* ****************************************************************************/
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// // Test approximate inference with an additional pruning step.
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// TEST(DCGaussianElimination, Incremental_approximate) {
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// Test approximate inference with an additional pruning step.
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TEST(HybridGaussianElimination, Incremental_approximate) {
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// Switching switching(5);
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// IncrementalHybrid incrementalHybrid;
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// HybridGaussianFactorGraph graph1;
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@ -395,12 +384,12 @@ TEST(HybridGaussianElimination, IncrementalInference) {
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// CHECK_EQUAL(2, actualBayesNet.size());
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// EXPECT_LONGS_EQUAL(10, actualBayesNet.atGaussian(0)->nrComponents());
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// EXPECT_LONGS_EQUAL(5, actualBayesNet.atGaussian(1)->nrComponents());
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// }
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}
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/* ************************************************************************/
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// // Test for figuring out the optimal ordering to ensure we get
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// // a discrete graph after elimination.
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// TEST(IncrementalHybrid, NonTrivial) {
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// Test for figuring out the optimal ordering to ensure we get
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// a discrete graph after elimination.
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TEST(IncrementalHybrid, NonTrivial) {
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// // This is a GTSAM-only test for running inference on a single legged
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// robot.
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// // The leg links are represented by the chain X-Y-Z-W, where X is the base
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@ -637,7 +626,7 @@ TEST(HybridGaussianElimination, IncrementalInference) {
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// auto lastConditional = boost::dynamic_pointer_cast<GaussianMixture>(
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// inc.hybridBayesNet().at(inc.hybridBayesNet().size() - 1));
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// EXPECT_LONGS_EQUAL(3, lastConditional->nrComponents());
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// }
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}
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/* ************************************************************************* */
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int main() {
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