additional test based on Frank's colab notebook

Author: varunagrawal

gtsam/hybrid/tests/testMixtureFactor.cpp

@@ -229,6 +229,82 @@ TEST(MixtureFactor, DifferentCovariances) {
   EXPECT(assert_equal(expected_values, actual_values));
 }
 
+/* ************************************************************************* */
+// 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<BetweenFactor<double>>(X(1), X(2), between, model0);
+  auto f1 =
+      std::make_shared<BetweenFactor<double>>(X(1), X(2), between, model1);
+  std::vector<NonlinearFactor::shared_ptr> 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<std::pair<Key, Matrix>> 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<std::pair<Key, Matrix>> 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<GaussianConditional>(terms0, 1, -d0, model0),
+       std::make_shared<GaussianConditional>(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<double>(X(1), x1, prior_noise).linearize(values);
+  mixture_fg.push_back(prior);
+
+  // bn.print("BayesNet:");
+  // mixture_fg.print("\n\n");
+
+  VectorValues vv{{X(1), x1 * I_1x1}, {X(2), x2 * I_1x1}};
+  // std::cout << "FG error for m1=0: "
+  //           << mixture_fg.error(HybridValues(vv, DiscreteValues{{m1.first, 0}}))
+  //           << std::endl;
+  // std::cout << "FG error for m1=1: "
+  //           << mixture_fg.error(HybridValues(vv, DiscreteValues{{m1.first, 1}}))
+  //           << std::endl;
+
+  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));
+  DiscreteValues dv;
+  dv.insert({M(1), 1});
+  HybridValues expected_values(cv, dv);
+
+  EXPECT(assert_equal(expected_values, actual_values));
+}
+
 /* ************************************************************************* */
 int main() {
   TestResult tr;