figured out how to get the correct continuous errors
Varun Agrawal
parent
a6d1a57478
commit
2f2f8c9ed5
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@ -69,56 +69,57 @@ Ordering getOrdering(HybridGaussianFactorGraph& factors,
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return ordering;
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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(HybridEstimation, Incremental) {
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size_t K = 15;
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std::vector<double> measurements = {0, 1, 2, 2, 2, 2, 3, 4, 5, 6, 6, 7, 8, 9, 9, 9, 10, 11, 11, 11, 11};
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// Ground truth discrete seq
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std::vector<size_t> discrete_seq = {1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0};
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Switching switching(K, 1.0, 0.1, measurements);
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HybridSmoother smoother;
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HybridNonlinearFactorGraph graph;
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Values initial;
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// /****************************************************************************/
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// // Test approximate inference with an additional pruning step.
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// TEST(HybridEstimation, Incremental) {
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// size_t K = 15;
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// std::vector<double> measurements = {0, 1, 2, 2, 2, 2, 3, 4, 5, 6, 6, 7, 8,
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// 9, 9, 9, 10, 11, 11, 11, 11};
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// // Ground truth discrete seq
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// std::vector<size_t> discrete_seq = {1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,
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// 0, 0, 1, 1, 0, 0, 0}; Switching switching(K, 1.0, 0.1, measurements);
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// HybridSmoother smoother;
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// HybridNonlinearFactorGraph graph;
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// Values initial;
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// Add the X(0) prior
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graph.push_back(switching.nonlinearFactorGraph.at(0));
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initial.insert(X(0), switching.linearizationPoint.at<double>(X(0)));
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// // Add the X(0) prior
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// graph.push_back(switching.nonlinearFactorGraph.at(0));
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// initial.insert(X(0), switching.linearizationPoint.at<double>(X(0)));
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HybridGaussianFactorGraph linearized;
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HybridGaussianFactorGraph bayesNet;
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// HybridGaussianFactorGraph linearized;
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// HybridGaussianFactorGraph bayesNet;
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for (size_t k = 1; k < K; k++) {
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// Motion Model
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graph.push_back(switching.nonlinearFactorGraph.at(k));
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// Measurement
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graph.push_back(switching.nonlinearFactorGraph.at(k + K - 1));
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// for (size_t k = 1; k < K; k++) {
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// // Motion Model
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// graph.push_back(switching.nonlinearFactorGraph.at(k));
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// // Measurement
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// graph.push_back(switching.nonlinearFactorGraph.at(k + K - 1));
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initial.insert(X(k), switching.linearizationPoint.at<double>(X(k)));
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// initial.insert(X(k), switching.linearizationPoint.at<double>(X(k)));
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bayesNet = smoother.hybridBayesNet();
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linearized = *graph.linearize(initial);
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Ordering ordering = getOrdering(bayesNet, linearized);
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// bayesNet = smoother.hybridBayesNet();
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// linearized = *graph.linearize(initial);
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// Ordering ordering = getOrdering(bayesNet, linearized);
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smoother.update(linearized, ordering, 3);
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graph.resize(0);
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}
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HybridValues delta = smoother.hybridBayesNet().optimize();
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// smoother.update(linearized, ordering, 3);
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// graph.resize(0);
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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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}
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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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* @brief A function to get a specific 1D robot motion problem as a linearized
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@ -188,6 +189,7 @@ TEST(HybridEstimation, Probability) {
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double between_sigma = 1.0, measurement_sigma = 0.1;
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std::vector<double> expected_errors, expected_prob_primes;
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for (size_t i = 0; i < pow(2, K - 1); i++) {
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std::vector<size_t> discrete_seq = getDiscreteSequence<K>(i);
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@ -195,19 +197,80 @@ TEST(HybridEstimation, Probability) {
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K, measurements, discrete_seq, measurement_sigma, between_sigma);
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auto bayes_net = linear_graph->eliminateSequential();
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// graph.print();
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// bayes_net->print();
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VectorValues values = bayes_net->optimize();
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std::cout << i << " : " << linear_graph->probPrime(values) << std::endl;
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expected_errors.push_back(linear_graph->error(values));
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expected_prob_primes.push_back(linear_graph->probPrime(values));
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std::cout << i << " : " << expected_errors.at(i) << "\t|\t"
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<< expected_prob_primes.at(i) << std::endl;
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}
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// std::vector<size_t> discrete_seq = getDiscreteSequence<K>(0);
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// GaussianFactorGraph::shared_ptr linear_graph = specificProblem(
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// K, measurements, discrete_seq, measurement_sigma, between_sigma);
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// auto bayes_net = linear_graph->eliminateSequential();
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// VectorValues values = bayes_net->optimize();
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// std::cout << "Total NLFG Error: " << linear_graph->error(values) << std::endl;
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// std::cout << "===============" << std::endl;
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Switching switching(K, between_sigma, measurement_sigma, measurements);
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auto graph = switching.linearizedFactorGraph;
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Ordering ordering = getOrdering(graph, HybridGaussianFactorGraph());
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HybridBayesNet::shared_ptr bayesNet = graph.eliminateSequential(ordering);
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const DecisionTreeFactor::shared_ptr decisionTree =
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bayesNet->discreteConditionals();
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decisionTree->print();
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HybridBayesNet::shared_ptr bayesNet;
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HybridGaussianFactorGraph::shared_ptr remainingGraph;
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Ordering continuous(graph.continuousKeys());
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std::tie(bayesNet, remainingGraph) =
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graph.eliminatePartialSequential(continuous);
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auto last_conditional = bayesNet->at(bayesNet->size() - 1);
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DiscreteKeys discrete_keys = last_conditional->discreteKeys();
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const std::vector<DiscreteValues> assignments =
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DiscreteValues::CartesianProduct(discrete_keys);
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vector<VectorValues::shared_ptr> vector_values;
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for (const DiscreteValues& assignment : assignments) {
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VectorValues values = bayesNet->optimize(assignment);
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vector_values.push_back(boost::make_shared<VectorValues>(values));
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}
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std::reverse(discrete_keys.begin(), discrete_keys.end());
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DecisionTree<Key, VectorValues::shared_ptr> delta_tree(discrete_keys,
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vector_values);
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vector<double> probPrimes;
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for (const DiscreteValues& assignment : assignments) {
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double error = 0.0;
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VectorValues delta = *delta_tree(assignment);
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for (auto factor : graph) {
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if (factor->isHybrid()) {
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auto f = boost::static_pointer_cast<GaussianMixtureFactor>(factor);
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error += f->error(delta, assignment);
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} else if (factor->isContinuous()) {
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auto f = boost::static_pointer_cast<HybridGaussianFactor>(factor);
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error += f->inner()->error(delta);
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}
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}
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// std::cout << "\n" << std::endl;
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// assignment.print();
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// std::cout << error << " | " << exp(-error) << std::endl;
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probPrimes.push_back(exp(-error));
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}
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AlgebraicDecisionTree<Key> expected_probPrimeTree(discrete_keys, probPrimes);
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expected_probPrimeTree.print("", DefaultKeyFormatter);
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// remainingGraph->add(DecisionTreeFactor(discrete_keys, probPrimeTree));
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// Ordering discrete(graph.discreteKeys());
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// // remainingGraph->print("remainingGraph");
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// // discrete.print();
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// auto discreteBayesNet = remainingGraph->eliminateSequential(discrete);
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// bayesNet->add(*discreteBayesNet);
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// // bayesNet->print();
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// HybridValues hybrid_values = bayesNet->optimize();
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// hybrid_values.discrete().print();
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}
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/* ************************************************************************* */
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