Merge branch 'develop' into hybrid-renaming
Varun Agrawal
commit
43f02ad176
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@ -31,7 +31,6 @@
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#include <CppUnitLite/TestHarness.h>
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using namespace gtsam;
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using noiseModel::Isotropic;
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using symbol_shorthand::M;
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using symbol_shorthand::X;
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using symbol_shorthand::Z;
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@ -18,7 +18,9 @@
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* @date December 2021
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*/
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#include <gtsam/base/Testable.h>
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#include <gtsam/base/TestableAssertions.h>
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#include <gtsam/discrete/DiscreteConditional.h>
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#include <gtsam/discrete/DiscreteValues.h>
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#include <gtsam/hybrid/HybridBayesNet.h>
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#include <gtsam/hybrid/HybridGaussianConditional.h>
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@ -27,16 +29,17 @@
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#include <gtsam/hybrid/HybridValues.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/linear/GaussianFactorGraph.h>
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#include <gtsam/linear/VectorValues.h>
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#include <gtsam/nonlinear/PriorFactor.h>
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#include <gtsam/slam/BetweenFactor.h>
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// Include for test suite
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#include <CppUnitLite/TestHarness.h>
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#include <memory>
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using namespace std;
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using namespace gtsam;
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using noiseModel::Isotropic;
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using symbol_shorthand::F;
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using symbol_shorthand::M;
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using symbol_shorthand::X;
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using symbol_shorthand::Z;
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@ -232,7 +235,7 @@ static HybridBayesNet GetGaussianMixtureModel(double mu0, double mu1,
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hbn.emplace_shared<HybridGaussianConditional>(
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KeyVector{z}, KeyVector{}, DiscreteKeys{m}, std::vector{c0, c1});
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auto mixing = make_shared<DiscreteConditional>(m, "0.5/0.5");
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auto mixing = make_shared<DiscreteConditional>(m, "50/50");
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hbn.push_back(mixing);
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return hbn;
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@ -278,7 +281,7 @@ TEST(HybridGaussianFactor, GaussianMixtureModel) {
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// At the halfway point between the means, we should get P(m|z)=0.5
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HybridBayesNet expected;
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expected.emplace_shared<DiscreteConditional>(m, "0.5/0.5");
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expected.emplace_shared<DiscreteConditional>(m, "50/50");
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EXPECT(assert_equal(expected, *bn));
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}
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@ -387,103 +390,134 @@ TEST(HybridGaussianFactor, GaussianMixtureModel2) {
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namespace test_two_state_estimation {
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/// Create Two State Bayes Network with measurements
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static HybridBayesNet CreateBayesNet(double mu0, double mu1, double sigma0,
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double sigma1,
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bool add_second_measurement = false,
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double prior_sigma = 1e-3,
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double measurement_sigma = 3.0) {
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DiscreteKey m1(M(1), 2);
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Key z0 = Z(0), z1 = Z(1);
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Key x0 = X(0), x1 = X(1);
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DiscreteKey m1(M(1), 2);
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HybridBayesNet hbn;
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void addMeasurement(HybridBayesNet& hbn, Key z_key, Key x_key, double sigma) {
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auto measurement_model = noiseModel::Isotropic::Sigma(1, sigma);
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hbn.emplace_shared<GaussianConditional>(z_key, Vector1(0.0), I_1x1, x_key,
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-I_1x1, measurement_model);
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}
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auto measurement_model = noiseModel::Isotropic::Sigma(1, measurement_sigma);
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// Add measurement P(z0 | x0)
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auto p_z0 = std::make_shared<GaussianConditional>(
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z0, Vector1(0.0), -I_1x1, x0, I_1x1, measurement_model);
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hbn.push_back(p_z0);
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// Add hybrid motion model
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/// Create hybrid motion model p(x1 | x0, m1)
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static GaussianMixture::shared_ptr CreateHybridMotionModel(double mu0,
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double mu1,
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double sigma0,
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double sigma1) {
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auto model0 = noiseModel::Isotropic::Sigma(1, sigma0);
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auto model1 = noiseModel::Isotropic::Sigma(1, sigma1);
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auto c0 = make_shared<GaussianConditional>(x1, Vector1(mu0), I_1x1, x0,
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auto c0 = make_shared<GaussianConditional>(X(1), Vector1(mu0), I_1x1, X(0),
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-I_1x1, model0),
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c1 = make_shared<GaussianConditional>(x1, Vector1(mu1), I_1x1, x0,
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c1 = make_shared<GaussianConditional>(X(1), Vector1(mu1), I_1x1, X(0),
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-I_1x1, model1);
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return std::make_shared<GaussianMixture>(
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KeyVector{X(1)}, KeyVector{X(0)}, DiscreteKeys{m1}, std::vector{c0, c1});
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}
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auto motion = std::make_shared<HybridGaussianConditional>(
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KeyVector{x1}, KeyVector{x0}, DiscreteKeys{m1}, std::vector{c0, c1});
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hbn.push_back(motion);
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/// Create two state Bayes network with 1 or two measurement models
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HybridBayesNet CreateBayesNet(
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const HybridGaussianConditional::shared_ptr& hybridMotionModel,
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bool add_second_measurement = false) {
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HybridBayesNet hbn;
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// Add measurement model p(z0 | x0)
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addMeasurement(hbn, Z(0), X(0), 3.0);
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// Optionally add second measurement model p(z1 | x1)
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if (add_second_measurement) {
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// Add second measurement
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auto p_z1 = std::make_shared<GaussianConditional>(
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z1, Vector1(0.0), -I_1x1, x1, I_1x1, measurement_model);
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hbn.push_back(p_z1);
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addMeasurement(hbn, Z(1), X(1), 3.0);
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}
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// Add hybrid motion model
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hbn.push_back(hybridMotionModel);
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// Discrete uniform prior.
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auto p_m1 = std::make_shared<DiscreteConditional>(m1, "0.5/0.5");
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hbn.push_back(p_m1);
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hbn.emplace_shared<DiscreteConditional>(m1, "50/50");
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return hbn;
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}
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/// Approximate the discrete marginal P(m1) using importance sampling
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std::pair<double, double> approximateDiscreteMarginal(
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const HybridBayesNet& hbn,
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const HybridGaussianConditional::shared_ptr& hybridMotionModel,
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const VectorValues& given, size_t N = 100000) {
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/// Create importance sampling network q(x0,x1,m) = p(x1|x0,m1) q(x0) P(m1),
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/// using q(x0) = N(z0, sigmaQ) to sample x0.
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HybridBayesNet q;
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q.push_back(hybridMotionModel); // Add hybrid motion model
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q.emplace_shared<GaussianConditional>(GaussianConditional::FromMeanAndStddev(
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X(0), given.at(Z(0)), /* sigmaQ = */ 3.0)); // Add proposal q(x0) for x0
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q.emplace_shared<DiscreteConditional>(m1, "50/50"); // Discrete prior.
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// Do importance sampling
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double w0 = 0.0, w1 = 0.0;
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std::mt19937_64 rng(42);
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for (int i = 0; i < N; i++) {
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HybridValues sample = q.sample(&rng);
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sample.insert(given);
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double weight = hbn.evaluate(sample) / q.evaluate(sample);
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(sample.atDiscrete(M(1)) == 0) ? w0 += weight : w1 += weight;
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}
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double pm1 = w1 / (w0 + w1);
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std::cout << "p(m0) = " << 100 * (1.0 - pm1) << std::endl;
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std::cout << "p(m1) = " << 100 * pm1 << std::endl;
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return {1.0 - pm1, pm1};
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}
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} // namespace test_two_state_estimation
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/* ************************************************************************* */
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/**
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* Test a model P(z0|x0)P(x1|x0,m1)P(z1|x1)P(m1).
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* Test a model p(z0|x0)p(z1|x1)p(x1|x0,m1)P(m1).
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*
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* P(f01|x1,x0,m1) has different means and same covariance.
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* p(x1|x0,m1) has mode-dependent mean but same covariance.
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*
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* Converting to a factor graph gives us
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* ϕ(x0)ϕ(x1,x0,m1)ϕ(x1)P(m1)
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* Converting to a factor graph gives us ϕ(x0;z0)ϕ(x1;z1)ϕ(x1,x0,m1)P(m1)
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*
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* If we only have a measurement on z0, then
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* the probability of m1 should be 0.5/0.5.
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* If we only have a measurement on x0, then
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* the posterior probability of m1 should be 0.5/0.5.
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* Getting a measurement on z1 gives use more information.
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*/
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TEST(HybridGaussianFactor, TwoStateModel) {
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using namespace test_two_state_estimation;
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double mu0 = 1.0, mu1 = 3.0;
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double sigma = 2.0;
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DiscreteKey m1(M(1), 2);
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Key z0 = Z(0), z1 = Z(1);
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double sigma = 0.5;
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auto hybridMotionModel = CreateHybridMotionModel(mu0, mu1, sigma, sigma);
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// Start with no measurement on x1, only on x0
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HybridBayesNet hbn = CreateBayesNet(mu0, mu1, sigma, sigma, false);
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const Vector1 z0(0.5);
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VectorValues given;
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given.insert(z0, Vector1(0.5));
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given.insert(Z(0), z0);
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{
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HybridBayesNet hbn = CreateBayesNet(hybridMotionModel);
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HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
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HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
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// Since no measurement on x1, we hedge our bets
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DiscreteConditional expected(m1, "0.5/0.5");
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// Importance sampling run with 100k samples gives 50.051/49.949
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// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
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DiscreteConditional expected(m1, "50/50");
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EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete())));
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}
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{
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// Now we add a measurement z1 on x1
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hbn = CreateBayesNet(mu0, mu1, sigma, sigma, true);
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// If we set z1=4.5 (>> 2.5 which is the halfway point),
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// probability of discrete mode should be leaning to m1==1.
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const Vector1 z1(4.5);
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given.insert(Z(1), z1);
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// If we see z1=2.6 (> 2.5 which is the halfway point),
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// discrete mode should say m1=1
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given.insert(z1, Vector1(2.6));
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HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
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HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
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HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
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// Since we have a measurement on z2, we get a definite result
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DiscreteConditional expected(m1, "0.49772729/0.50227271");
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// regression
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EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 1e-6));
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// Since we have a measurement on x1, we get a definite result
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// Values taken from an importance sampling run with 100k samples:
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// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
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DiscreteConditional expected(m1, "44.3854/55.6146");
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EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
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}
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}
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@ -504,85 +538,108 @@ TEST(HybridGaussianFactor, TwoStateModel2) {
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using namespace test_two_state_estimation;
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double mu0 = 1.0, mu1 = 3.0;
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double sigma0 = 6.0, sigma1 = 4.0;
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auto model0 = noiseModel::Isotropic::Sigma(1, sigma0);
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auto model1 = noiseModel::Isotropic::Sigma(1, sigma1);
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DiscreteKey m1(M(1), 2);
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Key z0 = Z(0), z1 = Z(1);
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double sigma0 = 0.5, sigma1 = 2.0;
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auto hybridMotionModel = CreateHybridMotionModel(mu0, mu1, sigma0, sigma1);
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// Start with no measurement on x1, only on x0
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HybridBayesNet hbn = CreateBayesNet(mu0, mu1, sigma0, sigma1, false);
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const Vector1 z0(0.5);
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VectorValues given;
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given.insert(z0, Vector1(0.5));
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given.insert(Z(0), z0);
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{
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// Start with no measurement on x1, only on x0
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HybridBayesNet hbn = CreateBayesNet(hybridMotionModel);
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HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
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{
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VectorValues vv{
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{X(0), Vector1(0.0)}, {X(1), Vector1(1.0)}, {Z(0), Vector1(0.5)}};
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HybridValues hv0(vv, DiscreteValues{{M(1), 0}}),
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hv1(vv, DiscreteValues{{M(1), 1}});
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EXPECT_DOUBLES_EQUAL(gfg.error(hv0) / hbn.error(hv0),
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gfg.error(hv1) / hbn.error(hv1), 1e-9);
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}
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{
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VectorValues vv{
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{X(0), Vector1(0.5)}, {X(1), Vector1(3.0)}, {Z(0), Vector1(0.5)}};
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HybridValues hv0(vv, DiscreteValues{{M(1), 0}}),
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hv1(vv, DiscreteValues{{M(1), 1}});
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// Check that ratio of Bayes net and factor graph for different modes is
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// equal for several values of {x0,x1}.
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for (VectorValues vv :
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{VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(1.0)}},
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VectorValues{{X(0), Vector1(0.5)}, {X(1), Vector1(3.0)}}}) {
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vv.insert(given); // add measurements for HBN
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HybridValues hv0(vv, {{M(1), 0}}), hv1(vv, {{M(1), 1}});
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EXPECT_DOUBLES_EQUAL(gfg.error(hv0) / hbn.error(hv0),
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gfg.error(hv1) / hbn.error(hv1), 1e-9);
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}
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HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
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// Importance sampling run with 100k samples gives 50.095/49.905
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// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
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// Since no measurement on x1, we a 50/50 probability
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auto p_m = bn->at(2)->asDiscrete();
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EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()(DiscreteValues{{m1.first, 0}}),
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1e-9);
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EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()(DiscreteValues{{m1.first, 1}}),
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1e-9);
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EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()({{M(1), 0}}), 1e-9);
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EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()({{M(1), 1}}), 1e-9);
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}
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{
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// Now we add a measurement z1 on x1
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hbn = CreateBayesNet(mu0, mu1, sigma0, sigma1, true);
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const Vector1 z1(4.0); // favors m==1
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given.insert(Z(1), z1);
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given.insert(z1, Vector1(2.2));
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HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
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HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
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{
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VectorValues vv{{X(0), Vector1(0.0)},
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{X(1), Vector1(1.0)},
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{Z(0), Vector1(0.5)},
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{Z(1), Vector1(2.2)}};
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HybridValues hv0(vv, DiscreteValues{{M(1), 0}}),
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hv1(vv, DiscreteValues{{M(1), 1}});
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EXPECT_DOUBLES_EQUAL(gfg.error(hv0) / hbn.error(hv0),
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gfg.error(hv1) / hbn.error(hv1), 1e-9);
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}
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{
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VectorValues vv{{X(0), Vector1(0.5)},
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{X(1), Vector1(3.0)},
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{Z(0), Vector1(0.5)},
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{Z(1), Vector1(2.2)}};
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HybridValues hv0(vv, DiscreteValues{{M(1), 0}}),
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hv1(vv, DiscreteValues{{M(1), 1}});
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// Check that ratio of Bayes net and factor graph for different modes is
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// equal for several values of {x0,x1}.
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for (VectorValues vv :
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{VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(1.0)}},
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VectorValues{{X(0), Vector1(0.5)}, {X(1), Vector1(3.0)}}}) {
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vv.insert(given); // add measurements for HBN
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HybridValues hv0(vv, {{M(1), 0}}), hv1(vv, {{M(1), 1}});
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EXPECT_DOUBLES_EQUAL(gfg.error(hv0) / hbn.error(hv0),
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gfg.error(hv1) / hbn.error(hv1), 1e-9);
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}
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HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
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// Since we have a measurement on z2, we get a definite result
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DiscreteConditional expected(m1, "0.44744586/0.55255414");
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// regression
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EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 1e-6));
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// Values taken from an importance sampling run with 100k samples:
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// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
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DiscreteConditional expected(m1, "48.3158/51.6842");
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EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
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}
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{
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// Add a different measurement z1 on x1 that favors m==0
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const Vector1 z1(1.1);
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given.insert_or_assign(Z(1), z1);
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HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
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HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
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HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
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// Values taken from an importance sampling run with 100k samples:
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// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
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DiscreteConditional expected(m1, "55.396/44.604");
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EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
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}
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}
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/* ************************************************************************* */
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/**
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* Same model, P(z0|x0)P(x1|x0,m1)P(z1|x1)P(m1), but now with very informative
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* measurements and vastly different motion model: either stand still or move
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* far. This yields a very informative posterior.
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*/
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TEST(GaussianMixtureFactor, TwoStateModel3) {
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using namespace test_two_state_estimation;
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double mu0 = 0.0, mu1 = 10.0;
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double sigma0 = 0.2, sigma1 = 5.0;
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auto hybridMotionModel = CreateHybridMotionModel(mu0, mu1, sigma0, sigma1);
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// We only check the 2-measurement case
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const Vector1 z0(0.0), z1(10.0);
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VectorValues given{{Z(0), z0}, {Z(1), z1}};
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HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
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HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
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HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
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// Values taken from an importance sampling run with 100k samples:
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// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
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DiscreteConditional expected(m1, "8.91527/91.0847");
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EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
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}
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/* ************************************************************************* */
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|
|
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Loading…
Reference in New Issue