get all but 2 tests passing
Varun Agrawal
parent
89079229bc
commit
16124f3617
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@ -19,6 +19,7 @@
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#include <gtsam/base/utilities.h>
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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/HybridEliminationTree.h>
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#include <gtsam/hybrid/HybridFactor.h>
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@ -274,9 +275,9 @@ TEST(HybridsGaussianElimination, Eliminate_x2) {
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EXPECT_LONGS_EQUAL(4, result.second->size());
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}
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/*
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****************************************************************************/
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// Helper method to generate gaussian factor graphs with a specific mode.
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/****************************************************************************
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* Helper method to generate gaussian factor graphs with a specific mode.
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*/
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GaussianFactorGraph::shared_ptr batchGFG(double between,
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Values linearizationPoint) {
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NonlinearFactorGraph graph;
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@ -290,8 +291,9 @@ GaussianFactorGraph::shared_ptr batchGFG(double between,
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return graph.linearize(linearizationPoint);
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}
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/*****************************************************************************/
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// Test elimination function by eliminating x1 and x2 in graph.
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/****************************************************************************
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* Test elimination function by eliminating x1 and x2 in graph.
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*/
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TEST(HybridGaussianElimination, EliminateHybrid_2_Variable) {
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Switching self(2, 1.0, 0.1);
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@ -371,287 +373,298 @@ TEST(HybridGaussianElimination, EliminateHybrid_2_Variable) {
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// EXPECT_DOUBLES_EQUAL(0.6125, actual, 1e-4);
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// }
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// /*
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// ****************************************************************************/
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// // Test partial elimination
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// TEST_UNSAFE(HybridFactorGraph, Partial_Elimination) {
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// Switching self(3);
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/****************************************************************************
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* Test partial elimination
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*/
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TEST(HybridFactorGraph, Partial_Elimination) {
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Switching self(3);
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// auto linearizedFactorGraph = self.linearizedFactorGraph;
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auto linearizedFactorGraph = self.linearizedFactorGraph;
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// // Create ordering.
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// Ordering ordering;
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// for (size_t k = 1; k <= self.K; k++) ordering += X(k);
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// Create ordering.
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Ordering ordering;
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for (size_t k = 1; k <= self.K; k++) ordering += X(k);
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// // Eliminate partially.
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// HybridBayesNet::shared_ptr hybridBayesNet;
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// HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
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// std::tie(hybridBayesNet, remainingFactorGraph) =
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// linearizedFactorGraph.eliminatePartialSequential(ordering);
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// Eliminate partially.
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HybridBayesNet::shared_ptr hybridBayesNet;
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HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
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std::tie(hybridBayesNet, remainingFactorGraph) =
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linearizedFactorGraph.eliminatePartialSequential(ordering);
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// CHECK(hybridBayesNet);
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// // GTSAM_PRINT(*hybridBayesNet); // HybridBayesNet
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// EXPECT_LONGS_EQUAL(3, hybridBayesNet->size());
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// EXPECT(hybridBayesNet->at(0)->frontals() == KeyVector{X(1)});
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// EXPECT(hybridBayesNet->at(0)->parents() == KeyVector({X(2), M(1)}));
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// EXPECT(hybridBayesNet->at(1)->frontals() == KeyVector{X(2)});
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// EXPECT(hybridBayesNet->at(1)->parents() == KeyVector({X(3), M(2), M(1)}));
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// EXPECT(hybridBayesNet->at(2)->frontals() == KeyVector{X(3)});
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// EXPECT(hybridBayesNet->at(2)->parents() == KeyVector({M(2), M(1)}));
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CHECK(hybridBayesNet);
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// GTSAM_PRINT(*hybridBayesNet); // HybridBayesNet
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EXPECT_LONGS_EQUAL(3, hybridBayesNet->size());
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EXPECT(hybridBayesNet->at(0)->frontals() == KeyVector{X(1)});
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EXPECT(hybridBayesNet->at(0)->parents() == KeyVector({X(2), M(1)}));
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EXPECT(hybridBayesNet->at(1)->frontals() == KeyVector{X(2)});
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EXPECT(hybridBayesNet->at(1)->parents() == KeyVector({X(3), M(1), M(2)}));
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EXPECT(hybridBayesNet->at(2)->frontals() == KeyVector{X(3)});
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EXPECT(hybridBayesNet->at(2)->parents() == KeyVector({M(1), M(2)}));
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// CHECK(remainingFactorGraph);
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// // GTSAM_PRINT(*remainingFactorGraph); // HybridFactorGraph
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// EXPECT_LONGS_EQUAL(3, remainingFactorGraph->size());
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// EXPECT(remainingFactorGraph->discreteGraph().at(0)->keys() ==
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// KeyVector({M(1)}));
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// EXPECT(remainingFactorGraph->discreteGraph().at(1)->keys() ==
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// KeyVector({M(2), M(1)}));
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// EXPECT(remainingFactorGraph->discreteGraph().at(2)->keys() ==
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// KeyVector({M(2), M(1)}));
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// }
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CHECK(remainingFactorGraph);
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// GTSAM_PRINT(*remainingFactorGraph); // HybridFactorGraph
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EXPECT_LONGS_EQUAL(3, remainingFactorGraph->size());
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EXPECT(remainingFactorGraph->at(0)->keys() == KeyVector({M(1)}));
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EXPECT(remainingFactorGraph->at(1)->keys() == KeyVector({M(2), M(1)}));
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EXPECT(remainingFactorGraph->at(2)->keys() == KeyVector({M(1), M(2)}));
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}
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// /*
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// ****************************************************************************/
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// // Test full elimination
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// TEST_UNSAFE(HybridFactorGraph, Full_Elimination) {
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// Switching self(3);
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/****************************************************************************
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* Test full elimination
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*/
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TEST(HybridFactorGraph, Full_Elimination) {
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Switching self(3);
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// auto linearizedFactorGraph = self.linearizedFactorGraph;
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auto linearizedFactorGraph = self.linearizedFactorGraph;
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// // We first do a partial elimination
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// HybridBayesNet::shared_ptr hybridBayesNet_partial;
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// HybridGaussianFactorGraph::shared_ptr remainingFactorGraph_partial;
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// DiscreteBayesNet discreteBayesNet;
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// We first do a partial elimination
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HybridBayesNet::shared_ptr hybridBayesNet_partial;
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HybridGaussianFactorGraph::shared_ptr remainingFactorGraph_partial;
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DiscreteBayesNet discreteBayesNet;
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// {
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// // Create ordering.
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// Ordering ordering;
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// for (size_t k = 1; k <= self.K; k++) ordering += X(k);
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{
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// Create ordering.
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Ordering ordering;
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for (size_t k = 1; k <= self.K; k++) ordering += X(k);
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// // Eliminate partially.
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// std::tie(hybridBayesNet_partial, remainingFactorGraph_partial) =
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// linearizedFactorGraph.eliminatePartialSequential(ordering);
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// Eliminate partially.
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std::tie(hybridBayesNet_partial, remainingFactorGraph_partial) =
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linearizedFactorGraph.eliminatePartialSequential(ordering);
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// DiscreteFactorGraph dfg;
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// dfg.push_back(remainingFactorGraph_partial->discreteGraph());
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// ordering.clear();
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// for (size_t k = 1; k < self.K; k++) ordering += M(k);
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// discreteBayesNet = *dfg.eliminateSequential(ordering, EliminateForMPE);
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// }
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DiscreteFactorGraph discrete_fg;
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//TODO(Varun) Make this a function of HybridGaussianFactorGraph?
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for(HybridFactor::shared_ptr& factor: (*remainingFactorGraph_partial)) {
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auto df = dynamic_pointer_cast<HybridDiscreteFactor>(factor);
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discrete_fg.push_back(df->inner());
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}
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ordering.clear();
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for (size_t k = 1; k < self.K; k++) ordering += M(k);
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discreteBayesNet = *discrete_fg.eliminateSequential(ordering, EliminateForMPE);
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}
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// // Create ordering.
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// Ordering ordering;
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// for (size_t k = 1; k <= self.K; k++) ordering += X(k);
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// for (size_t k = 1; k < self.K; k++) ordering += M(k);
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// Create ordering.
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Ordering ordering;
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for (size_t k = 1; k <= self.K; k++) ordering += X(k);
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for (size_t k = 1; k < self.K; k++) ordering += M(k);
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// // Eliminate partially.
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// HybridBayesNet::shared_ptr hybridBayesNet =
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// linearizedFactorGraph.eliminateSequential(ordering);
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// Eliminate partially.
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HybridBayesNet::shared_ptr hybridBayesNet =
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linearizedFactorGraph.eliminateSequential(ordering);
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// CHECK(hybridBayesNet);
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// EXPECT_LONGS_EQUAL(5, hybridBayesNet->size());
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// // p(x1 | x2, m1)
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// EXPECT(hybridBayesNet->at(0)->frontals() == KeyVector{X(1)});
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// EXPECT(hybridBayesNet->at(0)->parents() == KeyVector({X(2), M(1)}));
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// // p(x2 | x3, m1, m2)
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// EXPECT(hybridBayesNet->at(1)->frontals() == KeyVector{X(2)});
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// EXPECT(hybridBayesNet->at(1)->parents() == KeyVector({X(3), M(2), M(1)}));
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// // p(x3 | m1, m2)
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// EXPECT(hybridBayesNet->at(2)->frontals() == KeyVector{X(3)});
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// EXPECT(hybridBayesNet->at(2)->parents() == KeyVector({M(2), M(1)}));
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// // P(m1 | m2)
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// EXPECT(hybridBayesNet->at(3)->frontals() == KeyVector{M(1)});
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// EXPECT(hybridBayesNet->at(3)->parents() == KeyVector({M(2)}));
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// EXPECT(dynamic_pointer_cast<DiscreteConditional>(hybridBayesNet->at(3))
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// ->equals(*discreteBayesNet.at(0)));
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// // P(m2)
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// EXPECT(hybridBayesNet->at(4)->frontals() == KeyVector{M(2)});
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// EXPECT_LONGS_EQUAL(0, hybridBayesNet->at(4)->nrParents());
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// EXPECT(dynamic_pointer_cast<DiscreteConditional>(hybridBayesNet->at(4))
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// ->equals(*discreteBayesNet.at(1)));
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// }
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CHECK(hybridBayesNet);
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EXPECT_LONGS_EQUAL(5, hybridBayesNet->size());
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// p(x1 | x2, m1)
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EXPECT(hybridBayesNet->at(0)->frontals() == KeyVector{X(1)});
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EXPECT(hybridBayesNet->at(0)->parents() == KeyVector({X(2), M(1)}));
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// p(x2 | x3, m1, m2)
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EXPECT(hybridBayesNet->at(1)->frontals() == KeyVector{X(2)});
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EXPECT(hybridBayesNet->at(1)->parents() == KeyVector({X(3), M(1), M(2)}));
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// p(x3 | m1, m2)
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EXPECT(hybridBayesNet->at(2)->frontals() == KeyVector{X(3)});
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EXPECT(hybridBayesNet->at(2)->parents() == KeyVector({M(1), M(2)}));
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// P(m1 | m2)
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EXPECT(hybridBayesNet->at(3)->frontals() == KeyVector{M(1)});
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EXPECT(hybridBayesNet->at(3)->parents() == KeyVector({M(2)}));
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GTSAM_PRINT(*(hybridBayesNet->at(3)));
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GTSAM_PRINT(*(discreteBayesNet.at(0)));
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//TODO(Varun) FIX!!
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// EXPECT(
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// dynamic_pointer_cast<DiscreteConditional>(hybridBayesNet->at(3)->inner())
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// ->equals(*discreteBayesNet.at(0)));
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// // P(m2)
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// EXPECT(hybridBayesNet->at(4)->frontals() == KeyVector{M(2)});
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// EXPECT_LONGS_EQUAL(0, hybridBayesNet->at(4)->nrParents());
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// EXPECT(
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// dynamic_pointer_cast<DiscreteConditional>(hybridBayesNet->at(4)->inner())
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// ->equals(*discreteBayesNet.at(1)));
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}
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// /*
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// ****************************************************************************/
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// // Test printing
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// TEST(HybridFactorGraph, Printing) {
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// Switching self(3);
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/*
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****************************************************************************/
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// Test printing
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TEST(HybridFactorGraph, Printing) {
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Switching self(3);
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// auto linearizedFactorGraph = self.linearizedFactorGraph;
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auto linearizedFactorGraph = self.linearizedFactorGraph;
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// // Create ordering.
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// Ordering ordering;
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// for (size_t k = 1; k <= self.K; k++) ordering += X(k);
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// Create ordering.
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Ordering ordering;
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for (size_t k = 1; k <= self.K; k++) ordering += X(k);
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// // Eliminate partially.
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// HybridBayesNet::shared_ptr hybridBayesNet;
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// HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
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// std::tie(hybridBayesNet, remainingFactorGraph) =
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// linearizedFactorGraph.eliminatePartialSequential(ordering);
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// Eliminate partially.
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HybridBayesNet::shared_ptr hybridBayesNet;
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HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
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std::tie(hybridBayesNet, remainingFactorGraph) =
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linearizedFactorGraph.eliminatePartialSequential(ordering);
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// string expected_hybridFactorGraph = R"(
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// size: 8
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// DiscreteFactorGraph
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// size: 2
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// factor 0: P( m1 ):
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// Leaf 0.5
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// factor 1: P( m2 | m1 ):
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// Choice(m2)
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// 0 Choice(m1)
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// 0 0 Leaf 0.33333333
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// 0 1 Leaf 0.6
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// 1 Choice(m1)
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// 1 0 Leaf 0.66666667
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// 1 1 Leaf 0.4
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// DCFactorGraph
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// size: 2
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// factor 0: [ x1 x2; m1 ]{
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// Choice(m1)
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// 0 Leaf Jacobian factor on 2 keys:
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// A[x1] = [
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// -1
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// ]
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// A[x2] = [
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// 1
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// ]
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// b = [ -1 ]
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// No noise model
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// 1 Leaf Jacobian factor on 2 keys:
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// A[x1] = [
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// -1
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// ]
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// A[x2] = [
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// 1
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// ]
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// b = [ -0 ]
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// No noise model
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// }
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// factor 1: [ x2 x3; m2 ]{
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// Choice(m2)
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// 0 Leaf Jacobian factor on 2 keys:
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// A[x2] = [
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// -1
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// ]
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// A[x3] = [
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// 1
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// ]
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// b = [ -1 ]
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// No noise model
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// 1 Leaf Jacobian factor on 2 keys:
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// A[x2] = [
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// -1
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// ]
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// A[x3] = [
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// 1
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// ]
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// b = [ -0 ]
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// No noise model
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// }
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// GaussianGraph
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// size: 4
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// factor 0:
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// A[x1] = [
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// 10
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// ]
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// b = [ -10 ]
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// No noise model
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// factor 1:
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// A[x1] = [
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// 10
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// ]
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// b = [ -10 ]
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// No noise model
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// factor 2:
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// A[x2] = [
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// 10
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// ]
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// b = [ -10 ]
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// No noise model
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// factor 3:
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// A[x3] = [
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// 10
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// ]
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// b = [ -10 ]
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// No noise model
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// )";
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// EXPECT(assert_print_equal(expected_hybridFactorGraph,
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// linearizedFactorGraph));
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string expected_hybridFactorGraph = R"(
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size: 8
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factor 0: Continuous x1;
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// // Expected output for hybridBayesNet.
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// string expected_hybridBayesNet = R"(
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// size: 3
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// factor 0: GaussianMixture [ x1 | x2 m1 ]{
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// Choice(m1)
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// 0 Leaf Jacobian factor on 2 keys:
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// p(x1 | x2)
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// R = [ 14.1774 ]
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// S[x2] = [ -0.0705346 ]
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// d = [ -14.0364 ]
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// No noise model
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// 1 Leaf Jacobian factor on 2 keys:
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// p(x1 | x2)
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// R = [ 14.1774 ]
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// S[x2] = [ -0.0705346 ]
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// d = [ -14.1069 ]
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// No noise model
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// }
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// factor 1: GaussianMixture [ x2 | x3 m2 m1 ]{
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// Choice(m2)
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// 0 Choice(m1)
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// 0 0 Leaf Jacobian factor on 2 keys:
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// p(x2 | x3)
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// R = [ 10.0993 ]
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// S[x3] = [ -0.0990172 ]
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// d = [ -9.99975 ]
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// No noise model
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// 0 1 Leaf Jacobian factor on 2 keys:
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// p(x2 | x3)
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// R = [ 10.0993 ]
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// S[x3] = [ -0.0990172 ]
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// d = [ -9.90122 ]
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// No noise model
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// 1 Choice(m1)
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// 1 0 Leaf Jacobian factor on 2 keys:
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// p(x2 | x3)
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// R = [ 10.0993 ]
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// S[x3] = [ -0.0990172 ]
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// d = [ -10.0988 ]
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// No noise model
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// 1 1 Leaf Jacobian factor on 2 keys:
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// p(x2 | x3)
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// R = [ 10.0993 ]
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// S[x3] = [ -0.0990172 ]
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// d = [ -10.0002 ]
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// No noise model
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// }
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// factor 2: GaussianMixture [ x3 | m2 m1 ]{
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// Choice(m2)
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// 0 Choice(m1)
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// 0 0 Leaf Jacobian factor on 1 keys:
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// p(x3)
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// R = [ 10.0494 ]
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// d = [ -10.1489 ]
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// No noise model
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// 0 1 Leaf Jacobian factor on 1 keys:
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// p(x3)
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// R = [ 10.0494 ]
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// d = [ -10.1479 ]
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// No noise model
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// 1 Choice(m1)
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// 1 0 Leaf Jacobian factor on 1 keys:
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// p(x3)
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// R = [ 10.0494 ]
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// d = [ -10.0504 ]
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// No noise model
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// 1 1 Leaf Jacobian factor on 1 keys:
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// p(x3)
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// R = [ 10.0494 ]
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// d = [ -10.0494 ]
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// No noise model
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// }
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// )";
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// EXPECT(assert_print_equal(expected_hybridBayesNet, *hybridBayesNet));
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// }
|
||||
A[x1] = [
|
||||
10
|
||||
]
|
||||
b = [ -10 ]
|
||||
No noise model
|
||||
factor 1: Hybrid x1 x2 m1; m1 ]{
|
||||
Choice(m1)
|
||||
0 Leaf :
|
||||
A[x1] = [
|
||||
-1
|
||||
]
|
||||
A[x2] = [
|
||||
1
|
||||
]
|
||||
b = [ -1 ]
|
||||
No noise model
|
||||
|
||||
1 Leaf :
|
||||
A[x1] = [
|
||||
-1
|
||||
]
|
||||
A[x2] = [
|
||||
1
|
||||
]
|
||||
b = [ -0 ]
|
||||
No noise model
|
||||
|
||||
}
|
||||
factor 2: Hybrid x2 x3 m2; m2 ]{
|
||||
Choice(m2)
|
||||
0 Leaf :
|
||||
A[x2] = [
|
||||
-1
|
||||
]
|
||||
A[x3] = [
|
||||
1
|
||||
]
|
||||
b = [ -1 ]
|
||||
No noise model
|
||||
|
||||
1 Leaf :
|
||||
A[x2] = [
|
||||
-1
|
||||
]
|
||||
A[x3] = [
|
||||
1
|
||||
]
|
||||
b = [ -0 ]
|
||||
No noise model
|
||||
|
||||
}
|
||||
factor 3: Continuous x1;
|
||||
|
||||
A[x1] = [
|
||||
10
|
||||
]
|
||||
b = [ -10 ]
|
||||
No noise model
|
||||
factor 4: Continuous x2;
|
||||
|
||||
A[x2] = [
|
||||
10
|
||||
]
|
||||
b = [ -10 ]
|
||||
No noise model
|
||||
factor 5: Continuous x3;
|
||||
|
||||
A[x3] = [
|
||||
10
|
||||
]
|
||||
b = [ -10 ]
|
||||
No noise model
|
||||
factor 6: Discrete m1
|
||||
P( m1 ):
|
||||
Leaf 0.5
|
||||
|
||||
factor 7: Discrete m2 m1
|
||||
P( m2 | m1 ):
|
||||
Choice(m2)
|
||||
0 Choice(m1)
|
||||
0 0 Leaf 0.33333333
|
||||
0 1 Leaf 0.6
|
||||
1 Choice(m1)
|
||||
1 0 Leaf 0.66666667
|
||||
1 1 Leaf 0.4
|
||||
|
||||
)";
|
||||
EXPECT(assert_print_equal(expected_hybridFactorGraph, linearizedFactorGraph));
|
||||
|
||||
// Expected output for hybridBayesNet.
|
||||
string expected_hybridBayesNet = R"(
|
||||
size: 3
|
||||
factor 0: Hybrid P( x1 | x2 m1)
|
||||
Discrete Keys = (m1, 2),
|
||||
Choice(m1)
|
||||
0 Leaf p(x1 | x2)
|
||||
R = [ 14.1774 ]
|
||||
S[x2] = [ -0.0705346 ]
|
||||
d = [ -14.0364 ]
|
||||
No noise model
|
||||
|
||||
1 Leaf p(x1 | x2)
|
||||
R = [ 14.1774 ]
|
||||
S[x2] = [ -0.0705346 ]
|
||||
d = [ -14.1069 ]
|
||||
No noise model
|
||||
|
||||
factor 1: Hybrid P( x2 | x3 m1 m2)
|
||||
Discrete Keys = (m1, 2), (m2, 2),
|
||||
Choice(m2)
|
||||
0 Choice(m1)
|
||||
0 0 Leaf p(x2 | x3)
|
||||
R = [ 10.0993 ]
|
||||
S[x3] = [ -0.0990172 ]
|
||||
d = [ -9.99975 ]
|
||||
No noise model
|
||||
|
||||
0 1 Leaf p(x2 | x3)
|
||||
R = [ 10.0993 ]
|
||||
S[x3] = [ -0.0990172 ]
|
||||
d = [ -9.90122 ]
|
||||
No noise model
|
||||
|
||||
1 Choice(m1)
|
||||
1 0 Leaf p(x2 | x3)
|
||||
R = [ 10.0993 ]
|
||||
S[x3] = [ -0.0990172 ]
|
||||
d = [ -10.0988 ]
|
||||
No noise model
|
||||
|
||||
1 1 Leaf p(x2 | x3)
|
||||
R = [ 10.0993 ]
|
||||
S[x3] = [ -0.0990172 ]
|
||||
d = [ -10.0002 ]
|
||||
No noise model
|
||||
|
||||
factor 2: Hybrid P( x3 | m1 m2)
|
||||
Discrete Keys = (m1, 2), (m2, 2),
|
||||
Choice(m2)
|
||||
0 Choice(m1)
|
||||
0 0 Leaf p(x3)
|
||||
R = [ 10.0494 ]
|
||||
d = [ -10.1489 ]
|
||||
No noise model
|
||||
|
||||
0 1 Leaf p(x3)
|
||||
R = [ 10.0494 ]
|
||||
d = [ -10.1479 ]
|
||||
No noise model
|
||||
|
||||
1 Choice(m1)
|
||||
1 0 Leaf p(x3)
|
||||
R = [ 10.0494 ]
|
||||
d = [ -10.0504 ]
|
||||
No noise model
|
||||
|
||||
1 1 Leaf p(x3)
|
||||
R = [ 10.0494 ]
|
||||
d = [ -10.0494 ]
|
||||
No noise model
|
||||
|
||||
)";
|
||||
EXPECT(assert_print_equal(expected_hybridBayesNet, *hybridBayesNet));
|
||||
}
|
||||
|
||||
// /* *************************************************************************
|
||||
// */
|
||||
|
|
|
|||
Loading…
Reference in New Issue