From 32c67892ed618f719fdaf4a1f76befde90ef6ab5 Mon Sep 17 00:00:00 2001
From: Frank Dellaert <dellaert@gmail.com>
Date: Sat, 28 Sep 2024 20:09:24 -0700
Subject: [PATCH] Move test where it belongs

---
 gtsam/hybrid/tests/testHybridBayesNet.cpp     | 51 +++++++++++++++++++
 .../tests/testHybridGaussianFactorGraph.cpp   | 50 ------------------
 2 files changed, 51 insertions(+), 50 deletions(-)

diff --git a/gtsam/hybrid/tests/testHybridBayesNet.cpp b/gtsam/hybrid/tests/testHybridBayesNet.cpp
index ec8f08741..67005b470 100644
--- a/gtsam/hybrid/tests/testHybridBayesNet.cpp
+++ b/gtsam/hybrid/tests/testHybridBayesNet.cpp
@@ -444,6 +444,57 @@ TEST(HybridBayesNet, Sampling) {
   // num_samples)));
 }
 
+/* ****************************************************************************/
+// Test hybrid gaussian factor graph errorTree when
+// there is a HybridConditional in the graph
+TEST(HybridBayesNet, ErrorTreeWithConditional) {
+  using symbol_shorthand::F;
+
+  Key z0 = Z(0), f01 = F(0);
+  Key x0 = X(0), x1 = X(1);
+
+  HybridBayesNet hbn;
+
+  auto prior_model = noiseModel::Isotropic::Sigma(1, 1e-1);
+  auto measurement_model = noiseModel::Isotropic::Sigma(1, 2.0);
+
+  // Set a prior P(x0) at x0=0
+  hbn.emplace_shared<GaussianConditional>(x0, Vector1(0.0), I_1x1, prior_model);
+
+  // Add measurement P(z0 | x0)
+  hbn.emplace_shared<GaussianConditional>(z0, Vector1(0.0), -I_1x1, x0, I_1x1,
+                                          measurement_model);
+
+  // Add hybrid motion model
+  double mu = 0.0;
+  double sigma0 = 1e2, sigma1 = 1e-2;
+  auto model0 = noiseModel::Isotropic::Sigma(1, sigma0);
+  auto model1 = noiseModel::Isotropic::Sigma(1, sigma1);
+  auto c0 = make_shared<GaussianConditional>(f01, Vector1(mu), I_1x1, x1, I_1x1,
+                                             x0, -I_1x1, model0),
+       c1 = make_shared<GaussianConditional>(f01, Vector1(mu), I_1x1, x1, I_1x1,
+                                             x0, -I_1x1, model1);
+  DiscreteKey m1(M(2), 2);
+  hbn.emplace_shared<HybridGaussianConditional>(m1, std::vector{c0, c1});
+
+  // Discrete uniform prior.
+  hbn.emplace_shared<DiscreteConditional>(m1, "0.5/0.5");
+
+  VectorValues given;
+  given.insert(z0, Vector1(0.0));
+  given.insert(f01, Vector1(0.0));
+  auto gfg = hbn.toFactorGraph(given);
+
+  VectorValues vv;
+  vv.insert(x0, Vector1(1.0));
+  vv.insert(x1, Vector1(2.0));
+  AlgebraicDecisionTree<Key> errorTree = gfg.errorTree(vv);
+
+  // regression
+  AlgebraicDecisionTree<Key> expected(m1, 59.335390372, 5050.125);
+  EXPECT(assert_equal(expected, errorTree, 1e-9));
+}
+
 /* ************************************************************************* */
 int main() {
   TestResult tr;
diff --git a/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp b/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
index ee7ca5265..38429ac60 100644
--- a/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
+++ b/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
@@ -628,56 +628,6 @@ TEST(HybridGaussianFactorGraph, ErrorAndProbPrimeTree) {
   EXPECT(assert_equal(expected_probabilities, probabilities, 1e-7));
 }
 
-/* ****************************************************************************/
-// Test hybrid gaussian factor graph errorTree when
-// there is a HybridConditional in the graph
-TEST(HybridGaussianFactorGraph, ErrorTreeWithConditional) {
-  using symbol_shorthand::F;
-
-  Key z0 = Z(0), f01 = F(0);
-  Key x0 = X(0), x1 = X(1);
-
-  HybridBayesNet hbn;
-
-  auto prior_model = noiseModel::Isotropic::Sigma(1, 1e-1);
-  auto measurement_model = noiseModel::Isotropic::Sigma(1, 2.0);
-
-  // Set a prior P(x0) at x0=0
-  hbn.emplace_shared<GaussianConditional>(x0, Vector1(0.0), I_1x1, prior_model);
-
-  // Add measurement P(z0 | x0)
-  hbn.emplace_shared<GaussianConditional>(z0, Vector1(0.0), -I_1x1, x0, I_1x1,
-                                          measurement_model);
-
-  // Add hybrid motion model
-  double mu = 0.0;
-  double sigma0 = 1e2, sigma1 = 1e-2;
-  auto model0 = noiseModel::Isotropic::Sigma(1, sigma0);
-  auto model1 = noiseModel::Isotropic::Sigma(1, sigma1);
-  auto c0 = make_shared<GaussianConditional>(f01, Vector1(mu), I_1x1, x1, I_1x1,
-                                             x0, -I_1x1, model0),
-       c1 = make_shared<GaussianConditional>(f01, Vector1(mu), I_1x1, x1, I_1x1,
-                                             x0, -I_1x1, model1);
-  hbn.emplace_shared<HybridGaussianConditional>(m1, std::vector{c0, c1});
-
-  // Discrete uniform prior.
-  hbn.emplace_shared<DiscreteConditional>(m1, "0.5/0.5");
-
-  VectorValues given;
-  given.insert(z0, Vector1(0.0));
-  given.insert(f01, Vector1(0.0));
-  auto gfg = hbn.toFactorGraph(given);
-
-  VectorValues vv;
-  vv.insert(x0, Vector1(1.0));
-  vv.insert(x1, Vector1(2.0));
-  AlgebraicDecisionTree<Key> errorTree = gfg.errorTree(vv);
-
-  // regression
-  AlgebraicDecisionTree<Key> expected(m1, 59.335390372, 5050.125);
-  EXPECT(assert_equal(expected, errorTree, 1e-9));
-}
-
 /* ****************************************************************************/
 // Test hybrid gaussian factor graph errorTree during
 // incremental operation