move multifrontal optimize test to testHybridBayesTree and update docstrings

gtsam/hybrid/HybridGaussianFactorGraph.cpp

@@ -257,7 +257,6 @@ hybridElimination(const HybridGaussianFactorGraph &factors,
   // If there are no more continuous parents, then we should create here a
   // DiscreteFactor, with the error for each discrete choice.
   if (keysOfSeparator.empty()) {
-    // TODO(Varun) Use the math from the iMHS_Math-1-indexed document
     VectorValues empty_values;
     auto factorProb = [&](const GaussianFactor::shared_ptr &factor) {
       if (!factor) {
@@ -574,7 +573,7 @@ HybridGaussianFactorGraph::eliminateHybridSequential(
       bayesNet->at(bayesNet->size() - 1);
   DiscreteKeys discrete_keys = last_conditional->discreteKeys();
 
-  // If not discrete variables, return the eliminated bayes net.
+  // If no discrete variables, return the eliminated bayes net.
   if (discrete_keys.size() == 0) {
     return bayesNet;
   }

gtsam/hybrid/tests/testHybridBayesNet.cpp

@@ -164,25 +164,6 @@ TEST(HybridBayesNet, Optimize) {
   EXPECT(assert_equal(expectedValues, delta.continuous(), 1e-5));
 }
 
-/* ****************************************************************************/
-// Test bayes net multifrontal optimize
-TEST(HybridBayesNet, OptimizeMultifrontal) {
-  Switching s(4);
-
-  Ordering hybridOrdering = s.linearizedFactorGraph.getHybridOrdering();
-  HybridBayesTree::shared_ptr hybridBayesTree =
-      s.linearizedFactorGraph.eliminateMultifrontal(hybridOrdering);
-  HybridValues delta = hybridBayesTree->optimize();
-
-  VectorValues expectedValues;
-  expectedValues.insert(X(0), -0.999904 * Vector1::Ones());
-  expectedValues.insert(X(1), -0.99029 * Vector1::Ones());
-  expectedValues.insert(X(2), -1.00971 * Vector1::Ones());
-  expectedValues.insert(X(3), -1.0001 * Vector1::Ones());
-
-  EXPECT(assert_equal(expectedValues, delta.continuous(), 1e-5));
-}
-
 /* ****************************************************************************/
 // Test bayes net error
 TEST(HybridBayesNet, Error) {

gtsam/hybrid/tests/testHybridBayesTree.cpp

@@ -32,6 +32,25 @@ using noiseModel::Isotropic;
 using symbol_shorthand::M;
 using symbol_shorthand::X;
 
+/* ****************************************************************************/
+// Test multifrontal optimize
+TEST(HybridBayesTree, OptimizeMultifrontal) {
+  Switching s(4);
+
+  Ordering hybridOrdering = s.linearizedFactorGraph.getHybridOrdering();
+  HybridBayesTree::shared_ptr hybridBayesTree =
+      s.linearizedFactorGraph.eliminateMultifrontal(hybridOrdering);
+  HybridValues delta = hybridBayesTree->optimize();
+
+  VectorValues expectedValues;
+  expectedValues.insert(X(0), -0.999904 * Vector1::Ones());
+  expectedValues.insert(X(1), -0.99029 * Vector1::Ones());
+  expectedValues.insert(X(2), -1.00971 * Vector1::Ones());
+  expectedValues.insert(X(3), -1.0001 * Vector1::Ones());
+
+  EXPECT(assert_equal(expectedValues, delta.continuous(), 1e-5));
+}
+
 /* ****************************************************************************/
 // Test for optimizing a HybridBayesTree with a given assignment.
 TEST(HybridBayesTree, OptimizeAssignment) {

gtsam/hybrid/tests/testHybridNonlinearFactorGraph.cpp

@@ -386,11 +386,11 @@ TEST(HybridFactorGraph, Partial_Elimination) {
 
   auto linearizedFactorGraph = self.linearizedFactorGraph;
 
-  // Create ordering.
+  // Create ordering of only continuous variables.
   Ordering ordering;
   for (size_t k = 0; k < self.K; k++) ordering += X(k);
 
-  // Eliminate partially.
+  // Eliminate partially i.e. only continuous part.
   HybridBayesNet::shared_ptr hybridBayesNet;
   HybridGaussianFactorGraph::shared_ptr remainingFactorGraph;
   std::tie(hybridBayesNet, remainingFactorGraph) =