improve tests
Varun Agrawal
parent
8abd2756ea
commit
450fb0a016
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@ -38,6 +38,7 @@ using namespace gtsam;
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using noiseModel::Isotropic;
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using noiseModel::Isotropic;
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using symbol_shorthand::M;
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using symbol_shorthand::M;
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using symbol_shorthand::X;
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using symbol_shorthand::X;
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using symbol_shorthand::Z;
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/* ************************************************************************* */
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/* ************************************************************************* */
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// Check iterators of empty mixture.
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// Check iterators of empty mixture.
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@ -199,90 +200,161 @@ TEST(GaussianMixtureFactor, Error) {
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4.0, mixtureFactor.error({continuousValues, discreteValues}), 1e-9);
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4.0, mixtureFactor.error({continuousValues, discreteValues}), 1e-9);
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}
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}
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/**
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* @brief Helper function to specify a Hybrid Bayes Net
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* {P(X1) P(Z1 | X1, X2, M1)} and convert it to a Hybrid Factor Graph
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* {P(X1)L(X1, X2, M1; Z1)} by converting to likelihoods given Z1.
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*
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* We can specify either different means or different sigmas,
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* or both for each hybrid factor component.
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*
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* @param values Initial values for linearization.
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* @param means The mean values for the conditional components.
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* @param sigmas Noise model sigma values (standard deviation).
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* @param m1 The discrete mode key.
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* @param z1 The measurement value.
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* @return HybridGaussianFactorGraph
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*/
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HybridGaussianFactorGraph GetFactorGraphFromBayesNet(
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const gtsam::Values &values, const std::vector<double> &means,
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const std::vector<double> &sigmas, DiscreteKey &m1, double z1 = 0.0) {
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// Noise models
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auto model0 = noiseModel::Isotropic::Sigma(1, sigmas[0]);
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auto model1 = noiseModel::Isotropic::Sigma(1, sigmas[1]);
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auto prior_noise = noiseModel::Isotropic::Sigma(1, 1e-3);
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// GaussianMixtureFactor component factors
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auto f0 =
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std::make_shared<BetweenFactor<double>>(X(1), X(2), means[0], model0);
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auto f1 =
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std::make_shared<BetweenFactor<double>>(X(1), X(2), means[1], model1);
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std::vector<NonlinearFactor::shared_ptr> factors{f0, f1};
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/// Get terms for each p^m(z1 | x1, x2)
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Matrix H0_1, H0_2, H1_1, H1_2;
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double x1 = values.at<double>(X(1)), x2 = values.at<double>(X(2));
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Vector d0 = f0->evaluateError(x1, x2, &H0_1, &H0_2);
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std::vector<std::pair<Key, Matrix>> terms0 = {{Z(1), gtsam::I_1x1 /*Rx*/},
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//
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{X(1), H0_1 /*Sp1*/},
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{X(2), H0_2 /*Tp2*/}};
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Vector d1 = f1->evaluateError(x1, x2, &H1_1, &H1_2);
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std::vector<std::pair<Key, Matrix>> terms1 = {{Z(1), gtsam::I_1x1 /*Rx*/},
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//
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{X(1), H1_1 /*Sp1*/},
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{X(2), H1_2 /*Tp2*/}};
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// Create conditional P(Z1 | X1, X2, M1)
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auto gm = new gtsam::GaussianMixture(
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{Z(1)}, {X(1), X(2)}, {m1},
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{std::make_shared<GaussianConditional>(terms0, 1, -d0, model0),
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std::make_shared<GaussianConditional>(terms1, 1, -d1, model1)});
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gtsam::HybridBayesNet bn;
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bn.emplace_back(gm);
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// bn.print();
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// Create FG via toFactorGraph
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gtsam::VectorValues measurements;
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measurements.insert(Z(1), gtsam::I_1x1 * z1); // Set Z1 = 0
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HybridGaussianFactorGraph mixture_fg = bn.toFactorGraph(measurements);
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// Linearized prior factor on X1
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auto prior = PriorFactor<double>(X(1), x1, prior_noise).linearize(values);
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mixture_fg.push_back(prior);
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return mixture_fg;
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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// Test components with differing means
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/**
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TEST(GaussianMixtureFactor, DifferentMeans) {
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* @brief Test components with differing means.
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DiscreteKey m1(M(1), 2), m2(M(2), 2);
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*
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* We specify a hybrid Bayes network P(Z | X, M) =p(X1)p(Z1 | X1, X2, M1),
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* which is then converted to a factor graph by specifying Z1.
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*
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* p(Z1 | X1, X2, M1) has 2 factors each for the binary mode m1, with only the
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* means being different.
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*/
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TEST(GaussianMixtureFactor, DifferentMeansHBN) {
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DiscreteKey m1(M(1), 2);
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Values values;
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Values values;
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double x1 = 0.0, x2 = 1.75, x3 = 2.60;
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double x1 = 0.0, x2 = 1.75;
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values.insert(X(1), x1);
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values.insert(X(1), x1);
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values.insert(X(2), x2);
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values.insert(X(2), x2);
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values.insert(X(3), x3);
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auto model0 = noiseModel::Isotropic::Sigma(1, 1e-0);
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// Different means, same sigma
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auto model1 = noiseModel::Isotropic::Sigma(1, 1e-0);
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std::vector<double> means{0.0, 2.0}, sigmas{1e-0, 1e-0};
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auto prior_noise = noiseModel::Isotropic::Sigma(1, 1e-0);
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auto f0 = std::make_shared<BetweenFactor<double>>(X(1), X(2), 0.0, model0)
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HybridGaussianFactorGraph hfg =
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->linearize(values);
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GetFactorGraphFromBayesNet(values, means, sigmas, m1, 0.0);
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auto f1 = std::make_shared<BetweenFactor<double>>(X(1), X(2), 2.0, model1)
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->linearize(values);
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std::vector<GaussianFactor::shared_ptr> factors{f0, f1};
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GaussianMixtureFactor mixtureFactor({X(1), X(2)}, {m1}, factors, true);
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HybridGaussianFactorGraph hfg;
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hfg.push_back(mixtureFactor);
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f0 = std::make_shared<BetweenFactor<double>>(X(2), X(3), 0.0, model0)
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->linearize(values);
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f1 = std::make_shared<BetweenFactor<double>>(X(2), X(3), 2.0, model1)
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->linearize(values);
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std::vector<GaussianFactor::shared_ptr> factors23{f0, f1};
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hfg.push_back(GaussianMixtureFactor({X(2), X(3)}, {m2}, factors23, true));
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auto prior = PriorFactor<double>(X(1), x1, prior_noise).linearize(values);
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hfg.push_back(prior);
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hfg.push_back(PriorFactor<double>(X(2), 2.0, prior_noise).linearize(values));
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auto bn = hfg.eliminateSequential();
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HybridValues actual = bn->optimize();
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HybridValues expected(
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VectorValues{
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{X(1), Vector1(0.0)}, {X(2), Vector1(0.25)}, {X(3), Vector1(-0.6)}},
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DiscreteValues{{M(1), 1}, {M(2), 0}});
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EXPECT(assert_equal(expected, actual));
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{
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{
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DiscreteValues dv{{M(1), 0}, {M(2), 0}};
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// With no measurement on X2, each mode should be equally likely
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VectorValues cont = bn->optimize(dv);
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auto bn = hfg.eliminateSequential();
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double error = bn->error(HybridValues(cont, dv));
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HybridValues actual = bn->optimize();
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// regression
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EXPECT_DOUBLES_EQUAL(1.77418393408, error, 1e-9);
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HybridValues expected(
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VectorValues{{X(1), Vector1(0.0)}, {X(2), Vector1(-1.75)}},
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DiscreteValues{{M(1), 0}});
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EXPECT(assert_equal(expected, actual));
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{
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DiscreteValues dv{{M(1), 0}};
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VectorValues cont = bn->optimize(dv);
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double error = bn->error(HybridValues(cont, dv));
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// regression
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EXPECT_DOUBLES_EQUAL(0.69314718056, error, 1e-9);
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}
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{
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DiscreteValues dv{{M(1), 1}};
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VectorValues cont = bn->optimize(dv);
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double error = bn->error(HybridValues(cont, dv));
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// regression
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EXPECT_DOUBLES_EQUAL(0.69314718056, error, 1e-9);
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}
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}
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}
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{
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{
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DiscreteValues dv{{M(1), 0}, {M(2), 1}};
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// If we add a measurement on X2, we have more information to work with.
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VectorValues cont = bn->optimize(dv);
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// Add a measurement on X2
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double error = bn->error(HybridValues(cont, dv));
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auto prior_noise = noiseModel::Isotropic::Sigma(1, 1e-3);
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// regression
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GaussianConditional meas_z2(Z(2), Vector1(2.0), I_1x1, X(2), I_1x1,
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EXPECT_DOUBLES_EQUAL(1.77418393408, error, 1e-9);
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prior_noise);
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}
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auto prior_x2 = meas_z2.likelihood(Vector1(x2));
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{
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DiscreteValues dv{{M(1), 1}, {M(2), 0}};
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hfg.push_back(prior_x2);
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VectorValues cont = bn->optimize(dv);
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double error = bn->error(HybridValues(cont, dv));
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auto bn = hfg.eliminateSequential();
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// regression
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HybridValues actual = bn->optimize();
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EXPECT_DOUBLES_EQUAL(1.10751726741, error, 1e-9);
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}
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HybridValues expected(
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{
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VectorValues{{X(1), Vector1(0.0)}, {X(2), Vector1(0.25)}},
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DiscreteValues dv{{M(1), 1}, {M(2), 1}};
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DiscreteValues{{M(1), 1}});
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VectorValues cont = bn->optimize(dv);
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double error = bn->error(HybridValues(cont, dv));
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EXPECT(assert_equal(expected, actual));
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// regression
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EXPECT_DOUBLES_EQUAL(1.10751726741, error, 1e-9);
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{
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DiscreteValues dv{{M(1), 0}};
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VectorValues cont = bn->optimize(dv);
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double error = bn->error(HybridValues(cont, dv));
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// regression
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EXPECT_DOUBLES_EQUAL(2.12692448787, error, 1e-9);
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}
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{
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DiscreteValues dv{{M(1), 1}};
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VectorValues cont = bn->optimize(dv);
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double error = bn->error(HybridValues(cont, dv));
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// regression
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EXPECT_DOUBLES_EQUAL(0.126928487854, error, 1e-9);
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}
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}
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}
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}
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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/**
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/**
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* @brief Test components with differing covariances.
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* @brief Test components with differing covariances
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* The factor graph is
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* but with a Bayes net P(Z|X, M) converted to a FG.
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* *-X1-*-X2
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* |
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* M1
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*/
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*/
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TEST(GaussianMixtureFactor, DifferentCovariances) {
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TEST(GaussianMixtureFactor, DifferentCovariances) {
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DiscreteKey m1(M(1), 2);
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DiscreteKey m1(M(1), 2);
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@ -292,123 +364,9 @@ TEST(GaussianMixtureFactor, DifferentCovariances) {
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values.insert(X(1), x1);
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values.insert(X(1), x1);
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values.insert(X(2), x2);
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values.insert(X(2), x2);
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double between = 0.0;
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std::vector<double> means{0.0, 0.0}, sigmas{1e2, 1e-2};
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HybridGaussianFactorGraph mixture_fg =
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auto model0 = noiseModel::Isotropic::Sigma(1, 1e2);
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GetFactorGraphFromBayesNet(values, means, sigmas, m1);
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auto model1 = noiseModel::Isotropic::Sigma(1, 1e-2);
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auto prior_noise = noiseModel::Isotropic::Sigma(1, 1e-3);
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auto f0 =
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std::make_shared<BetweenFactor<double>>(X(1), X(2), between, model0);
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auto f1 =
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std::make_shared<BetweenFactor<double>>(X(1), X(2), between, model1);
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std::vector<NonlinearFactor::shared_ptr> factors{f0, f1};
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// Create via toFactorGraph
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using symbol_shorthand::Z;
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Matrix H0_1, H0_2, H1_1, H1_2;
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Vector d0 = f0->evaluateError(x1, x2, &H0_1, &H0_2);
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std::vector<std::pair<Key, Matrix>> terms0 = {{Z(1), gtsam::I_1x1 /*Rx*/},
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//
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{X(1), H0_1 /*Sp1*/},
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{X(2), H0_2 /*Tp2*/}};
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Vector d1 = f1->evaluateError(x1, x2, &H1_1, &H1_2);
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std::vector<std::pair<Key, Matrix>> terms1 = {{Z(1), gtsam::I_1x1 /*Rx*/},
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//
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{X(1), H1_1 /*Sp1*/},
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{X(2), H1_2 /*Tp2*/}};
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gtsam::GaussianMixtureFactor gmf(
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{X(1), X(2)}, {m1},
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{std::make_shared<JacobianFactor>(X(1), H0_1, X(2), H0_2, -d0, model0),
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std::make_shared<JacobianFactor>(X(1), H1_1, X(2), H1_2, -d1, model1)},
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true);
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// Create FG with single GaussianMixtureFactor
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HybridGaussianFactorGraph mixture_fg;
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mixture_fg.add(gmf);
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// Linearized prior factor on X1
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auto prior = PriorFactor<double>(X(1), x1, prior_noise).linearize(values);
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mixture_fg.push_back(prior);
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auto hbn = mixture_fg.eliminateSequential();
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// hbn->print();
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VectorValues cv;
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cv.insert(X(1), Vector1(0.0));
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cv.insert(X(2), Vector1(0.0));
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// Check that the error values at the MLE point μ.
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AlgebraicDecisionTree<Key> errorTree = hbn->errorTree(cv);
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DiscreteValues dv0{{M(1), 0}};
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DiscreteValues dv1{{M(1), 1}};
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// regression
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EXPECT_DOUBLES_EQUAL(9.90348755254, errorTree(dv0), 1e-9);
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EXPECT_DOUBLES_EQUAL(0.69314718056, errorTree(dv1), 1e-9);
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DiscreteConditional expected_m1(m1, "0.5/0.5");
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DiscreteConditional actual_m1 = *(hbn->at(2)->asDiscrete());
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EXPECT(assert_equal(expected_m1, actual_m1));
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}
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/* ************************************************************************* */
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/**
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* @brief Test components with differing covariances
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* but with a Bayes net P(Z|X, M) converted to a FG.
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*/
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TEST(GaussianMixtureFactor, DifferentCovariances2) {
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DiscreteKey m1(M(1), 2);
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Values values;
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double x1 = 1.0, x2 = 1.0;
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values.insert(X(1), x1);
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values.insert(X(2), x2);
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double between = 0.0;
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auto model0 = noiseModel::Isotropic::Sigma(1, 1e2);
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auto model1 = noiseModel::Isotropic::Sigma(1, 1e-2);
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auto prior_noise = noiseModel::Isotropic::Sigma(1, 1e-3);
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auto f0 =
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std::make_shared<BetweenFactor<double>>(X(1), X(2), between, model0);
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auto f1 =
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std::make_shared<BetweenFactor<double>>(X(1), X(2), between, model1);
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std::vector<NonlinearFactor::shared_ptr> factors{f0, f1};
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// Create via toFactorGraph
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using symbol_shorthand::Z;
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Matrix H0_1, H0_2, H1_1, H1_2;
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Vector d0 = f0->evaluateError(x1, x2, &H0_1, &H0_2);
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std::vector<std::pair<Key, Matrix>> terms0 = {{Z(1), gtsam::I_1x1 /*Rx*/},
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//
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{X(1), H0_1 /*Sp1*/},
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{X(2), H0_2 /*Tp2*/}};
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Vector d1 = f1->evaluateError(x1, x2, &H1_1, &H1_2);
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std::vector<std::pair<Key, Matrix>> terms1 = {{Z(1), gtsam::I_1x1 /*Rx*/},
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//
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{X(1), H1_1 /*Sp1*/},
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{X(2), H1_2 /*Tp2*/}};
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auto gm = new gtsam::GaussianMixture(
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{Z(1)}, {X(1), X(2)}, {m1},
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{std::make_shared<GaussianConditional>(terms0, 1, -d0, model0),
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std::make_shared<GaussianConditional>(terms1, 1, -d1, model1)});
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gtsam::HybridBayesNet bn;
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bn.emplace_back(gm);
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gtsam::VectorValues measurements;
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measurements.insert(Z(1), gtsam::Z_1x1);
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// Create FG with single GaussianMixtureFactor
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HybridGaussianFactorGraph mixture_fg = bn.toFactorGraph(measurements);
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// Linearized prior factor on X1
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auto prior = PriorFactor<double>(X(1), x1, prior_noise).linearize(values);
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mixture_fg.push_back(prior);
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auto hbn = mixture_fg.eliminateSequential();
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auto hbn = mixture_fg.eliminateSequential();
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||||||
|
|
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||||||
Loading…
Reference in New Issue