Pulled out hybrid motion model tests
Frank Dellaert
parent
6d57055c71
commit
71655cc3cf
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@ -208,354 +208,7 @@ TEST(HybridGaussianFactor, Error) {
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4.0, hybridFactor.error({continuousValues, discreteValues}), 1e-9);
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}
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namespace test_two_state_estimation {
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DiscreteKey m1(M(1), 2);
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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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/// Create hybrid motion model p(x1 | x0, m1)
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static HybridGaussianConditional::shared_ptr CreateHybridMotionModel(
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double mu0, double mu1, double sigma0, 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>(X(1), Vector1(mu0), I_1x1, X(0),
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-I_1x1, model0),
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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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DiscreteKeys discreteParents{m1};
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return std::make_shared<HybridGaussianConditional>(
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discreteParents, HybridGaussianConditional::Conditionals(
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discreteParents, std::vector{c0, c1}));
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}
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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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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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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(z1|x1)p(x1|x0,m1)P(m1).
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*
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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 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 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 = 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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const Vector1 z0(0.5);
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VectorValues given;
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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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// 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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// 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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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 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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/* ************************************************************************* */
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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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*
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* P(x1|x0,m1) has different means and different covariances.
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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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*
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* If we only have a measurement on z0, then
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* the P(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, 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 = 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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const Vector1 z0(0.5);
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VectorValues given;
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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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// 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()({{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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const Vector1 z1(4.0); // favors m==1
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given.insert(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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// 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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// 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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* Test a model p(z0|x0)p(x1|x0,m1)p(z1|x1)p(m1).
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*
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* p(x1|x0,m1) has the same means but different covariances.
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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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*
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* If we only have a measurement on z0, then
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* the p(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, TwoStateModel3) {
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using namespace test_two_state_estimation;
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double mu = 1.0;
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double sigma0 = 0.5, sigma1 = 2.0;
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auto hybridMotionModel = CreateHybridMotionModel(mu, mu, sigma0, sigma1);
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// Start with no measurement on x1, only on x0
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const Vector1 z0(0.5);
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VectorValues given;
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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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// 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()({{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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const Vector1 z1(4.0); // favors m==1
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given.insert(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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// 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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// 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, "51.7762/48.2238");
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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==1
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const Vector1 z1(7.0);
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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, "49.0762/50.9238");
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EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.005));
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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(HybridGaussianFactor, TwoStateModel4) {
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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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namespace test_direct_factor_graph {
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/**
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* @brief Create a Factor Graph by directly specifying all
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@ -0,0 +1,385 @@
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file testHybridMotionModel.cpp
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* @brief Tests hybrid inference with a simple switching motion model
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* @author Varun Agrawal
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* @author Fan Jiang
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* @author Frank Dellaert
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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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#include <gtsam/hybrid/HybridGaussianFactor.h>
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#include <gtsam/hybrid/HybridGaussianFactorGraph.h>
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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 symbol_shorthand::M;
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using symbol_shorthand::X;
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using symbol_shorthand::Z;
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DiscreteKey m1(M(1), 2);
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|
||||
void addMeasurement(HybridBayesNet &hbn, Key z_key, Key x_key, double sigma) {
|
||||
auto measurement_model = noiseModel::Isotropic::Sigma(1, sigma);
|
||||
hbn.emplace_shared<GaussianConditional>(z_key, Vector1(0.0), I_1x1, x_key,
|
||||
-I_1x1, measurement_model);
|
||||
}
|
||||
|
||||
/// Create hybrid motion model p(x1 | x0, m1)
|
||||
static HybridGaussianConditional::shared_ptr CreateHybridMotionModel(
|
||||
double mu0, double mu1, double sigma0, double sigma1) {
|
||||
auto model0 = noiseModel::Isotropic::Sigma(1, sigma0);
|
||||
auto model1 = noiseModel::Isotropic::Sigma(1, sigma1);
|
||||
auto c0 = make_shared<GaussianConditional>(X(1), Vector1(mu0), I_1x1, X(0),
|
||||
-I_1x1, model0),
|
||||
c1 = make_shared<GaussianConditional>(X(1), Vector1(mu1), I_1x1, X(0),
|
||||
-I_1x1, model1);
|
||||
return std::make_shared<HybridGaussianConditional>(m1, std::vector{c0, c1});
|
||||
}
|
||||
|
||||
/// Create two state Bayes network with 1 or two measurement models
|
||||
HybridBayesNet CreateBayesNet(
|
||||
const HybridGaussianConditional::shared_ptr &hybridMotionModel,
|
||||
bool add_second_measurement = false) {
|
||||
HybridBayesNet hbn;
|
||||
|
||||
// Add measurement model p(z0 | x0)
|
||||
addMeasurement(hbn, Z(0), X(0), 3.0);
|
||||
|
||||
// Optionally add second measurement model p(z1 | x1)
|
||||
if (add_second_measurement) {
|
||||
addMeasurement(hbn, Z(1), X(1), 3.0);
|
||||
}
|
||||
|
||||
// Add hybrid motion model
|
||||
hbn.push_back(hybridMotionModel);
|
||||
|
||||
// Discrete uniform prior.
|
||||
hbn.emplace_shared<DiscreteConditional>(m1, "50/50");
|
||||
|
||||
return hbn;
|
||||
}
|
||||
|
||||
/// Approximate the discrete marginal P(m1) using importance sampling
|
||||
std::pair<double, double> approximateDiscreteMarginal(
|
||||
const HybridBayesNet &hbn,
|
||||
const HybridGaussianConditional::shared_ptr &hybridMotionModel,
|
||||
const VectorValues &given, size_t N = 100000) {
|
||||
/// Create importance sampling network q(x0,x1,m) = p(x1|x0,m1) q(x0) P(m1),
|
||||
/// using q(x0) = N(z0, sigmaQ) to sample x0.
|
||||
HybridBayesNet q;
|
||||
q.push_back(hybridMotionModel); // Add hybrid motion model
|
||||
q.emplace_shared<GaussianConditional>(GaussianConditional::FromMeanAndStddev(
|
||||
X(0), given.at(Z(0)), /* sigmaQ = */ 3.0)); // Add proposal q(x0) for x0
|
||||
q.emplace_shared<DiscreteConditional>(m1, "50/50"); // Discrete prior.
|
||||
|
||||
// Do importance sampling
|
||||
double w0 = 0.0, w1 = 0.0;
|
||||
std::mt19937_64 rng(42);
|
||||
for (int i = 0; i < N; i++) {
|
||||
HybridValues sample = q.sample(&rng);
|
||||
sample.insert(given);
|
||||
double weight = hbn.evaluate(sample) / q.evaluate(sample);
|
||||
(sample.atDiscrete(M(1)) == 0) ? w0 += weight : w1 += weight;
|
||||
}
|
||||
double pm1 = w1 / (w0 + w1);
|
||||
std::cout << "p(m0) = " << 100 * (1.0 - pm1) << std::endl;
|
||||
std::cout << "p(m1) = " << 100 * pm1 << std::endl;
|
||||
return {1.0 - pm1, pm1};
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* Test a model p(z0|x0)p(z1|x1)p(x1|x0,m1)P(m1).
|
||||
*
|
||||
* p(x1|x0,m1) has mode-dependent mean but same covariance.
|
||||
*
|
||||
* Converting to a factor graph gives us ϕ(x0;z0)ϕ(x1;z1)ϕ(x1,x0,m1)P(m1)
|
||||
*
|
||||
* If we only have a measurement on x0, then
|
||||
* the posterior probability of m1 should be 0.5/0.5.
|
||||
* Getting a measurement on z1 gives use more information.
|
||||
*/
|
||||
TEST(HybridGaussianFactor, TwoStateModel) {
|
||||
double mu0 = 1.0, mu1 = 3.0;
|
||||
double sigma = 0.5;
|
||||
auto hybridMotionModel = CreateHybridMotionModel(mu0, mu1, sigma, sigma);
|
||||
|
||||
// Start with no measurement on x1, only on x0
|
||||
const Vector1 z0(0.5);
|
||||
|
||||
VectorValues given;
|
||||
given.insert(Z(0), z0);
|
||||
|
||||
{
|
||||
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel);
|
||||
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
|
||||
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
|
||||
|
||||
// Since no measurement on x1, we hedge our bets
|
||||
// Importance sampling run with 100k samples gives 50.051/49.949
|
||||
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
|
||||
DiscreteConditional expected(m1, "50/50");
|
||||
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete())));
|
||||
}
|
||||
|
||||
{
|
||||
// If we set z1=4.5 (>> 2.5 which is the halfway point),
|
||||
// probability of discrete mode should be leaning to m1==1.
|
||||
const Vector1 z1(4.5);
|
||||
given.insert(Z(1), z1);
|
||||
|
||||
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
|
||||
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
|
||||
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
|
||||
|
||||
// Since we have a measurement on x1, we get a definite result
|
||||
// Values taken from an importance sampling run with 100k samples:
|
||||
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
|
||||
DiscreteConditional expected(m1, "44.3854/55.6146");
|
||||
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
|
||||
}
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* Test a model P(z0|x0)P(x1|x0,m1)P(z1|x1)P(m1).
|
||||
*
|
||||
* P(x1|x0,m1) has different means and different covariances.
|
||||
*
|
||||
* Converting to a factor graph gives us
|
||||
* ϕ(x0)ϕ(x1,x0,m1)ϕ(x1)P(m1)
|
||||
*
|
||||
* If we only have a measurement on z0, then
|
||||
* the P(m1) should be 0.5/0.5.
|
||||
* Getting a measurement on z1 gives use more information.
|
||||
*/
|
||||
TEST(HybridGaussianFactor, TwoStateModel2) {
|
||||
double mu0 = 1.0, mu1 = 3.0;
|
||||
double sigma0 = 0.5, sigma1 = 2.0;
|
||||
auto hybridMotionModel = CreateHybridMotionModel(mu0, mu1, sigma0, sigma1);
|
||||
|
||||
// Start with no measurement on x1, only on x0
|
||||
const Vector1 z0(0.5);
|
||||
VectorValues given;
|
||||
given.insert(Z(0), z0);
|
||||
|
||||
{
|
||||
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel);
|
||||
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
|
||||
|
||||
// Check that ratio of Bayes net and factor graph for different modes is
|
||||
// equal for several values of {x0,x1}.
|
||||
for (VectorValues vv :
|
||||
{VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(1.0)}},
|
||||
VectorValues{{X(0), Vector1(0.5)}, {X(1), Vector1(3.0)}}}) {
|
||||
vv.insert(given); // add measurements for HBN
|
||||
HybridValues hv0(vv, {{M(1), 0}}), hv1(vv, {{M(1), 1}});
|
||||
EXPECT_DOUBLES_EQUAL(gfg.error(hv0) / hbn.error(hv0),
|
||||
gfg.error(hv1) / hbn.error(hv1), 1e-9);
|
||||
}
|
||||
|
||||
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
|
||||
|
||||
// Importance sampling run with 100k samples gives 50.095/49.905
|
||||
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
|
||||
|
||||
// Since no measurement on x1, we a 50/50 probability
|
||||
auto p_m = bn->at(2)->asDiscrete();
|
||||
EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()({{M(1), 0}}), 1e-9);
|
||||
EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()({{M(1), 1}}), 1e-9);
|
||||
}
|
||||
|
||||
{
|
||||
// Now we add a measurement z1 on x1
|
||||
const Vector1 z1(4.0); // favors m==1
|
||||
given.insert(Z(1), z1);
|
||||
|
||||
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
|
||||
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
|
||||
|
||||
// Check that ratio of Bayes net and factor graph for different modes is
|
||||
// equal for several values of {x0,x1}.
|
||||
for (VectorValues vv :
|
||||
{VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(1.0)}},
|
||||
VectorValues{{X(0), Vector1(0.5)}, {X(1), Vector1(3.0)}}}) {
|
||||
vv.insert(given); // add measurements for HBN
|
||||
HybridValues hv0(vv, {{M(1), 0}}), hv1(vv, {{M(1), 1}});
|
||||
EXPECT_DOUBLES_EQUAL(gfg.error(hv0) / hbn.error(hv0),
|
||||
gfg.error(hv1) / hbn.error(hv1), 1e-9);
|
||||
}
|
||||
|
||||
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
|
||||
|
||||
// Values taken from an importance sampling run with 100k samples:
|
||||
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
|
||||
DiscreteConditional expected(m1, "48.3158/51.6842");
|
||||
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
|
||||
}
|
||||
|
||||
{
|
||||
// Add a different measurement z1 on x1 that favors m==0
|
||||
const Vector1 z1(1.1);
|
||||
given.insert_or_assign(Z(1), z1);
|
||||
|
||||
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
|
||||
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
|
||||
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
|
||||
|
||||
// Values taken from an importance sampling run with 100k samples:
|
||||
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
|
||||
DiscreteConditional expected(m1, "55.396/44.604");
|
||||
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
|
||||
}
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* Test a model p(z0|x0)p(x1|x0,m1)p(z1|x1)p(m1).
|
||||
*
|
||||
* p(x1|x0,m1) has the same means but different covariances.
|
||||
*
|
||||
* Converting to a factor graph gives us
|
||||
* ϕ(x0)ϕ(x1,x0,m1)ϕ(x1)p(m1)
|
||||
*
|
||||
* If we only have a measurement on z0, then
|
||||
* the p(m1) should be 0.5/0.5.
|
||||
* Getting a measurement on z1 gives use more information.
|
||||
*/
|
||||
TEST(HybridGaussianFactor, TwoStateModel3) {
|
||||
double mu = 1.0;
|
||||
double sigma0 = 0.5, sigma1 = 2.0;
|
||||
auto hybridMotionModel = CreateHybridMotionModel(mu, mu, sigma0, sigma1);
|
||||
|
||||
// Start with no measurement on x1, only on x0
|
||||
const Vector1 z0(0.5);
|
||||
VectorValues given;
|
||||
given.insert(Z(0), z0);
|
||||
|
||||
{
|
||||
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel);
|
||||
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
|
||||
|
||||
// Check that ratio of Bayes net and factor graph for different modes is
|
||||
// equal for several values of {x0,x1}.
|
||||
for (VectorValues vv :
|
||||
{VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(1.0)}},
|
||||
VectorValues{{X(0), Vector1(0.5)}, {X(1), Vector1(3.0)}}}) {
|
||||
vv.insert(given); // add measurements for HBN
|
||||
HybridValues hv0(vv, {{M(1), 0}}), hv1(vv, {{M(1), 1}});
|
||||
EXPECT_DOUBLES_EQUAL(gfg.error(hv0) / hbn.error(hv0),
|
||||
gfg.error(hv1) / hbn.error(hv1), 1e-9);
|
||||
}
|
||||
|
||||
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
|
||||
|
||||
// Importance sampling run with 100k samples gives 50.095/49.905
|
||||
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
|
||||
|
||||
// Since no measurement on x1, we a 50/50 probability
|
||||
auto p_m = bn->at(2)->asDiscrete();
|
||||
EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()({{M(1), 0}}), 1e-9);
|
||||
EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()({{M(1), 1}}), 1e-9);
|
||||
}
|
||||
|
||||
{
|
||||
// Now we add a measurement z1 on x1
|
||||
const Vector1 z1(4.0); // favors m==1
|
||||
given.insert(Z(1), z1);
|
||||
|
||||
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
|
||||
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
|
||||
|
||||
// Check that ratio of Bayes net and factor graph for different modes is
|
||||
// equal for several values of {x0,x1}.
|
||||
for (VectorValues vv :
|
||||
{VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(1.0)}},
|
||||
VectorValues{{X(0), Vector1(0.5)}, {X(1), Vector1(3.0)}}}) {
|
||||
vv.insert(given); // add measurements for HBN
|
||||
HybridValues hv0(vv, {{M(1), 0}}), hv1(vv, {{M(1), 1}});
|
||||
EXPECT_DOUBLES_EQUAL(gfg.error(hv0) / hbn.error(hv0),
|
||||
gfg.error(hv1) / hbn.error(hv1), 1e-9);
|
||||
}
|
||||
|
||||
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
|
||||
|
||||
// Values taken from an importance sampling run with 100k samples:
|
||||
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
|
||||
DiscreteConditional expected(m1, "51.7762/48.2238");
|
||||
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
|
||||
}
|
||||
|
||||
{
|
||||
// Add a different measurement z1 on x1 that favors m==1
|
||||
const Vector1 z1(7.0);
|
||||
given.insert_or_assign(Z(1), z1);
|
||||
|
||||
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
|
||||
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
|
||||
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
|
||||
|
||||
// Values taken from an importance sampling run with 100k samples:
|
||||
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
|
||||
DiscreteConditional expected(m1, "49.0762/50.9238");
|
||||
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.005));
|
||||
}
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
/**
|
||||
* Same model, P(z0|x0)P(x1|x0,m1)P(z1|x1)P(m1), but now with very informative
|
||||
* measurements and vastly different motion model: either stand still or move
|
||||
* far. This yields a very informative posterior.
|
||||
*/
|
||||
TEST(HybridGaussianFactor, TwoStateModel4) {
|
||||
double mu0 = 0.0, mu1 = 10.0;
|
||||
double sigma0 = 0.2, sigma1 = 5.0;
|
||||
auto hybridMotionModel = CreateHybridMotionModel(mu0, mu1, sigma0, sigma1);
|
||||
|
||||
// We only check the 2-measurement case
|
||||
const Vector1 z0(0.0), z1(10.0);
|
||||
VectorValues given{{Z(0), z0}, {Z(1), z1}};
|
||||
|
||||
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
|
||||
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
|
||||
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
|
||||
|
||||
// Values taken from an importance sampling run with 100k samples:
|
||||
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
|
||||
DiscreteConditional expected(m1, "8.91527/91.0847");
|
||||
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
int main() {
|
||||
TestResult tr;
|
||||
return TestRegistry::runAllTests(tr);
|
||||
}
|
||||
/* ************************************************************************* */
|
||||
Loading…
Reference in New Issue