Tests which verify direct factor specification works well

gtsam/hybrid/tests/testGaussianMixtureFactor.cpp

@@ -388,6 +388,151 @@ TEST(GaussianMixtureFactor, DifferentCovariances) {
   EXPECT(assert_equal(expected_m1, actual_m1));
 }
 
+HybridGaussianFactorGraph CreateFactorGraph(const gtsam::Values &values,
+                                            const std::vector<double> &mus,
+                                            const std::vector<double> &sigmas,
+                                            DiscreteKey &m1) {
+  auto model0 = noiseModel::Isotropic::Sigma(1, sigmas[0]);
+  auto model1 = noiseModel::Isotropic::Sigma(1, sigmas[1]);
+  auto prior_noise = noiseModel::Isotropic::Sigma(1, 1e-3);
+
+  auto f0 = std::make_shared<BetweenFactor<double>>(X(0), X(1), mus[0], model0)
+                ->linearize(values);
+  auto f1 = std::make_shared<BetweenFactor<double>>(X(0), X(1), mus[1], model1)
+                ->linearize(values);
+
+  // Create GaussianMixtureFactor
+  std::vector<GaussianFactor::shared_ptr> factors{f0, f1};
+  AlgebraicDecisionTree<Key> logNormalizers(
+      {m1}, std::vector<double>{ComputeLogNormalizer(model0),
+                                ComputeLogNormalizer(model1)});
+  GaussianMixtureFactor mixtureFactor({X(0), X(1)}, {m1}, factors,
+                                      logNormalizers);
+
+  HybridGaussianFactorGraph hfg;
+  hfg.push_back(mixtureFactor);
+
+  hfg.push_back(PriorFactor<double>(X(0), values.at<double>(X(0)), prior_noise)
+                    .linearize(values));
+
+  return hfg;
+}
+
+TEST(GaussianMixtureFactor, DifferentMeansFG) {
+  DiscreteKey m1(M(1), 2);
+
+  Values values;
+  double x1 = 0.0, x2 = 1.75;
+  values.insert(X(0), x1);
+  values.insert(X(1), x2);
+
+  std::vector<double> mus = {0.0, 2.0}, sigmas = {1e-0, 1e-0};
+
+  HybridGaussianFactorGraph hfg = CreateFactorGraph(values, mus, sigmas, m1);
+
+  {
+    auto bn = hfg.eliminateSequential();
+    HybridValues actual = bn->optimize();
+
+    HybridValues expected(
+        VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(-1.75)}},
+        DiscreteValues{{M(1), 0}});
+
+    EXPECT(assert_equal(expected, actual));
+
+    {
+      DiscreteValues dv{{M(1), 0}};
+      VectorValues cont = bn->optimize(dv);
+      double error = bn->error(HybridValues(cont, dv));
+      // regression
+      EXPECT_DOUBLES_EQUAL(0.69314718056, error, 1e-9);
+    }
+    {
+      DiscreteValues dv{{M(1), 1}};
+      VectorValues cont = bn->optimize(dv);
+      double error = bn->error(HybridValues(cont, dv));
+      // regression
+      EXPECT_DOUBLES_EQUAL(0.69314718056, error, 1e-9);
+    }
+  }
+
+  {
+    auto prior_noise = noiseModel::Isotropic::Sigma(1, 1e-3);
+    hfg.push_back(
+        PriorFactor<double>(X(1), mus[1], prior_noise).linearize(values));
+
+    auto bn = hfg.eliminateSequential();
+    HybridValues actual = bn->optimize();
+
+    HybridValues expected(
+        VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(0.25)}},
+        DiscreteValues{{M(1), 1}});
+
+    EXPECT(assert_equal(expected, actual));
+
+    {
+      DiscreteValues dv{{M(1), 0}};
+      VectorValues cont = bn->optimize(dv);
+      double error = bn->error(HybridValues(cont, dv));
+      // regression
+      EXPECT_DOUBLES_EQUAL(2.12692448787, error, 1e-9);
+    }
+    {
+      DiscreteValues dv{{M(1), 1}};
+      VectorValues cont = bn->optimize(dv);
+      double error = bn->error(HybridValues(cont, dv));
+      // regression
+      EXPECT_DOUBLES_EQUAL(0.126928487854, error, 1e-9);
+    }
+  }
+}
+
+/* ************************************************************************* */
+/**
+ * @brief Test components with differing covariances.
+ * The factor graph is
+ *     *-X1-*-X2
+ *          |
+ *          M1
+ */
+TEST(GaussianMixtureFactor, DifferentCovariancesFG) {
+  DiscreteKey m1(M(1), 2);
+
+  Values values;
+  double x1 = 1.0, x2 = 1.0;
+  values.insert(X(0), x1);
+  values.insert(X(1), x2);
+
+  std::vector<double> mus = {0.0, 0.0}, sigmas = {1e2, 1e-2};
+
+  // Create FG with GaussianMixtureFactor and prior on X1
+  HybridGaussianFactorGraph mixture_fg =
+      CreateFactorGraph(values, mus, sigmas, m1);
+
+  auto hbn = mixture_fg.eliminateSequential();
+
+  VectorValues cv;
+  cv.insert(X(0), Vector1(0.0));
+  cv.insert(X(1), Vector1(0.0));
+
+  // Check that the error values at the MLE point μ.
+  AlgebraicDecisionTree<Key> errorTree = hbn->errorTree(cv);
+  hbn->errorTree(cv).print();
+  hbn2->errorTree(cv).print();
+
+  DiscreteValues dv0{{M(1), 0}};
+  DiscreteValues dv1{{M(1), 1}};
+
+  // regression
+  EXPECT_DOUBLES_EQUAL(9.90348755254, errorTree(dv0), 1e-9);
+  EXPECT_DOUBLES_EQUAL(0.69314718056, errorTree(dv1), 1e-9);
+
+  DiscreteConditional expected_m1(m1, "0.5/0.5");
+  DiscreteConditional actual_m1 = *(hbn->at(2)->asDiscrete());
+
+  EXPECT(assert_equal(expected_m1, actual_m1));
+}
+
 /* ************************************************************************* */
 int main() {
   TestResult tr;