kill test since we no longer need it

gtsam/hybrid/tests/testHybridSmoother.cpp

@@ -70,56 +70,6 @@ Switching InitializeEstimationProblem(
 
 }  // namespace estimation_fixture
 
-/****************************************************************************/
-// Test HybridSmoother::addConditionals
-TEST(HybridSmoother, AddConditionals) {
-  using namespace estimation_fixture;
-
-  size_t K = 7;
-
-  // Switching example of robot moving in 1D
-  // with given measurements and equal mode priors.
-  HybridNonlinearFactorGraph graph;
-  Values initial;
-  Switching switching = InitializeEstimationProblem(
-      K, 1.0, 0.1, measurements, "1/1 1/1", &graph, &initial);
-
-  HybridSmoother smoother(0.5);
-  constexpr size_t maxNrLeaves = 5;
-
-  // Loop over timesteps from 1...K-1
-  for (size_t k = 1; k < K; k++) {
-    if (k > 1) graph.push_back(switching.modeChain.at(k - 1));  // Mode chain
-    graph.push_back(switching.binaryFactors.at(k - 1));         // Motion Model
-    graph.push_back(switching.unaryFactors.at(k));              // Measurement
-
-    // Add a loop closure factor every 3rd step between `k-3` and `k`.
-    if (k % 3 == 0) {
-      size_t l = int(k / 3);
-      auto open = std::make_shared<MotionModel>(
-               X(k - 3), X(k), 0.0, noiseModel::Isotropic::Sigma(1, 10.0)),
-           close = std::make_shared<MotionModel>(
-               X(k - 3), X(k), 0.0, noiseModel::Isotropic::Sigma(1, 0.1));
-
-      HybridNonlinearFactor loopFactor(
-          DiscreteKey(L(l), 2),
-          std::vector<NoiseModelFactor::shared_ptr>{open, close});
-      graph.push_back(loopFactor);
-    }
-
-    initial.insert(X(k), switching.linearizationPoint.at<double>(X(k)));
-
-    HybridGaussianFactorGraph linearized = *graph.linearize(initial);
-
-    smoother.update(linearized, maxNrLeaves);
-
-    // Clear all the factors from the graph
-    graph.resize(0);
-  }
-
-  //TODO(Varun) recreate scenario which would fail before commit b627fc422.
-}
-
 /****************************************************************************/
 // Test approximate inference with an additional pruning step.
 TEST(HybridSmoother, IncrementalSmoother) {