diff --git a/gtsam/hybrid/HybridBayesTree.cpp b/gtsam/hybrid/HybridBayesTree.cpp index f08eff01b..da2645b5a 100644 --- a/gtsam/hybrid/HybridBayesTree.cpp +++ b/gtsam/hybrid/HybridBayesTree.cpp @@ -136,8 +136,7 @@ struct HybridAssignmentData { } }; -/* ************************************************************************* - */ +/* ************************************************************************* */ GaussianBayesTree HybridBayesTree::choose( const DiscreteValues& assignment) const { GaussianBayesTree gbt; @@ -157,8 +156,12 @@ GaussianBayesTree HybridBayesTree::choose( return gbt; } -/* ************************************************************************* - */ +/* ************************************************************************* */ +double HybridBayesTree::error(const HybridValues& values) const { + return HybridGaussianFactorGraph(*this).error(values); +} + +/* ************************************************************************* */ VectorValues HybridBayesTree::optimize(const DiscreteValues& assignment) const { GaussianBayesTree gbt = this->choose(assignment); // If empty GaussianBayesTree, means a clique is pruned hence invalid diff --git a/gtsam/hybrid/HybridBayesTree.h b/gtsam/hybrid/HybridBayesTree.h index af8eb3228..ab2d8a73d 100644 --- a/gtsam/hybrid/HybridBayesTree.h +++ b/gtsam/hybrid/HybridBayesTree.h @@ -85,9 +85,7 @@ class GTSAM_EXPORT HybridBayesTree : public BayesTree { GaussianBayesTree choose(const DiscreteValues& assignment) const; /** Error for all conditionals. */ - double error(const HybridValues& values) const { - return HybridGaussianFactorGraph(*this).error(values); - } + double error(const HybridValues& values) const; /** * @brief Optimize the hybrid Bayes tree by computing the MPE for the current diff --git a/gtsam/hybrid/tests/testMixtureFactor.cpp b/gtsam/hybrid/tests/testMixtureFactor.cpp index 52f56f62e..0b2564403 100644 --- a/gtsam/hybrid/tests/testMixtureFactor.cpp +++ b/gtsam/hybrid/tests/testMixtureFactor.cpp @@ -115,221 +115,6 @@ TEST(MixtureFactor, Dim) { EXPECT_LONGS_EQUAL(1, mixtureFactor.dim()); } -/* ************************************************************************* */ -// Test components with differing means -TEST(MixtureFactor, DifferentMeans) { - DiscreteKey m1(M(1), 2), m2(M(2), 2); - - Values values; - double x1 = 0.0, x2 = 1.75, x3 = 2.60; - values.insert(X(1), x1); - values.insert(X(2), x2); - values.insert(X(3), x3); - - auto model0 = noiseModel::Isotropic::Sigma(1, 1e-0); - auto model1 = noiseModel::Isotropic::Sigma(1, 1e-0); - auto prior_noise = noiseModel::Isotropic::Sigma(1, 1e-0); - - auto f0 = std::make_shared>(X(1), X(2), 0.0, model0); - auto f1 = std::make_shared>(X(1), X(2), 2.0, model1); - std::vector factors{f0, f1}; - - MixtureFactor mixtureFactor({X(1), X(2)}, {m1}, factors); - HybridNonlinearFactorGraph hnfg; - hnfg.push_back(mixtureFactor); - - f0 = std::make_shared>(X(2), X(3), 0.0, model0); - f1 = std::make_shared>(X(2), X(3), 2.0, model1); - std::vector factors23{f0, f1}; - hnfg.push_back(MixtureFactor({X(2), X(3)}, {m2}, factors23)); - - auto prior = PriorFactor(X(1), x1, prior_noise); - hnfg.push_back(prior); - - hnfg.emplace_shared>(X(2), 2.0, prior_noise); - - auto hgfg = hnfg.linearize(values); - auto bn = hgfg->eliminateSequential(); - HybridValues actual = bn->optimize(); - - HybridValues expected( - VectorValues{ - {X(1), Vector1(0.0)}, {X(2), Vector1(0.25)}, {X(3), Vector1(-0.6)}}, - DiscreteValues{{M(1), 1}, {M(2), 0}}); - - EXPECT(assert_equal(expected, actual)); - - { - DiscreteValues dv{{M(1), 0}, {M(2), 0}}; - VectorValues cont = bn->optimize(dv); - double error = bn->error(HybridValues(cont, dv)); - // regression - EXPECT_DOUBLES_EQUAL(1.77418393408, error, 1e-9); - } - { - DiscreteValues dv{{M(1), 0}, {M(2), 1}}; - VectorValues cont = bn->optimize(dv); - double error = bn->error(HybridValues(cont, dv)); - // regression - EXPECT_DOUBLES_EQUAL(1.77418393408, error, 1e-9); - } - { - DiscreteValues dv{{M(1), 1}, {M(2), 0}}; - VectorValues cont = bn->optimize(dv); - double error = bn->error(HybridValues(cont, dv)); - // regression - EXPECT_DOUBLES_EQUAL(1.10751726741, error, 1e-9); - } - { - DiscreteValues dv{{M(1), 1}, {M(2), 1}}; - VectorValues cont = bn->optimize(dv); - double error = bn->error(HybridValues(cont, dv)); - // regression - EXPECT_DOUBLES_EQUAL(1.10751726741, error, 1e-9); - } -} - -/* ************************************************************************* */ -// Test components with differing covariances -TEST(MixtureFactor, DifferentCovariances) { - DiscreteKey m1(M(1), 2); - - Values values; - double x1 = 1.0, x2 = 1.0; - values.insert(X(1), x1); - values.insert(X(2), x2); - - double between = 0.0; - - auto model0 = noiseModel::Isotropic::Sigma(1, 1e2); - auto model1 = noiseModel::Isotropic::Sigma(1, 1e-2); - auto prior_noise = noiseModel::Isotropic::Sigma(1, 1e-3); - - auto f0 = - std::make_shared>(X(1), X(2), between, model0); - auto f1 = - std::make_shared>(X(1), X(2), between, model1); - std::vector factors{f0, f1}; - - // Create via toFactorGraph - using symbol_shorthand::Z; - Matrix H0_1, H0_2, H1_1, H1_2; - Vector d0 = f0->evaluateError(x1, x2, &H0_1, &H0_2); - std::vector> terms0 = {{Z(1), gtsam::I_1x1 /*Rx*/}, - // - {X(1), H0_1 /*Sp1*/}, - {X(2), H0_2 /*Tp2*/}}; - - Vector d1 = f1->evaluateError(x1, x2, &H1_1, &H1_2); - std::vector> terms1 = {{Z(1), gtsam::I_1x1 /*Rx*/}, - // - {X(1), H1_1 /*Sp1*/}, - {X(2), H1_2 /*Tp2*/}}; - auto gm = new gtsam::GaussianMixture( - {Z(1)}, {X(1), X(2)}, {m1}, - {std::make_shared(terms0, 1, -d0, model0), - std::make_shared(terms1, 1, -d1, model1)}); - gtsam::HybridBayesNet bn; - bn.emplace_back(gm); - - gtsam::VectorValues measurements; - measurements.insert(Z(1), gtsam::Z_1x1); - // Create FG with single GaussianMixtureFactor - HybridGaussianFactorGraph mixture_fg = bn.toFactorGraph(measurements); - - // Linearized prior factor on X1 - auto prior = PriorFactor(X(1), x1, prior_noise).linearize(values); - mixture_fg.push_back(prior); - - auto hbn = mixture_fg.eliminateSequential(); - - VectorValues cv; - cv.insert(X(1), Vector1(0.0)); - cv.insert(X(2), Vector1(0.0)); - - // Check that we get different error values at the MLE point μ. - AlgebraicDecisionTree errorTree = hbn->errorTree(cv); - - HybridValues hv0(cv, DiscreteValues{{M(1), 0}}); - HybridValues hv1(cv, DiscreteValues{{M(1), 1}}); - - auto cond0 = hbn->at(0)->asMixture(); - auto cond1 = hbn->at(1)->asMixture(); - auto discrete_cond = hbn->at(2)->asDiscrete(); - AlgebraicDecisionTree expectedErrorTree(m1, 9.90348755254, - 0.69314718056); - EXPECT(assert_equal(expectedErrorTree, errorTree)); -} - -/* ************************************************************************* */ -// Test components with differing means and covariances -TEST(MixtureFactor, DifferentMeansAndCovariances) { - DiscreteKey m1(M(1), 2); - - Values values; - double x1 = 0.0, x2 = 7.0; - values.insert(X(1), x1); - - double between = 0.0; - - auto model0 = noiseModel::Isotropic::Sigma(1, 1e2); - auto model1 = noiseModel::Isotropic::Sigma(1, 1e-2); - auto prior_noise = noiseModel::Isotropic::Sigma(1, 1e-3); - - auto f0 = - std::make_shared>(X(1), X(2), between, model0); - auto f1 = - std::make_shared>(X(1), X(2), between, model1); - std::vector factors{f0, f1}; - - // Create via toFactorGraph - using symbol_shorthand::Z; - Matrix H0_1, H0_2, H1_1, H1_2; - Vector d0 = f0->evaluateError(x1, x2, &H0_1, &H0_2); - std::vector> terms0 = {{Z(1), gtsam::I_1x1 /*Rx*/}, - // - {X(1), H0_1 /*Sp1*/}, - {X(2), H0_2 /*Tp2*/}}; - - Vector d1 = f1->evaluateError(x1, x2, &H1_1, &H1_2); - std::vector> terms1 = {{Z(1), gtsam::I_1x1 /*Rx*/}, - // - {X(1), H1_1 /*Sp1*/}, - {X(2), H1_2 /*Tp2*/}}; - auto gm = new gtsam::GaussianMixture( - {Z(1)}, {X(1), X(2)}, {m1}, - {std::make_shared(terms0, 1, -d0, model0), - std::make_shared(terms1, 1, -d1, model1)}); - gtsam::HybridBayesNet bn; - bn.emplace_back(gm); - - gtsam::VectorValues measurements; - measurements.insert(Z(1), gtsam::Z_1x1); - // Create FG with single GaussianMixtureFactor - HybridGaussianFactorGraph mixture_fg = bn.toFactorGraph(measurements); - - // Linearized prior factor on X1 - auto prior = PriorFactor(X(1), x1, prior_noise).linearize(values); - mixture_fg.push_back(prior); - - VectorValues vv{{X(1), x1 * I_1x1}, {X(2), x2 * I_1x1}}; - - auto hbn = mixture_fg.eliminateSequential(); - - HybridValues actual_values = hbn->optimize(); - - VectorValues cv; - cv.insert(X(1), Vector1(0.0)); - cv.insert(X(2), Vector1(-7.0)); - - // The first value is chosen as the tiebreaker - DiscreteValues dv; - dv.insert({M(1), 0}); - HybridValues expected_values(cv, dv); - - EXPECT(assert_equal(expected_values, actual_values)); -} - /* ************************************************************************* */ int main() { TestResult tr;