Some test refactoring

gtsam/hybrid/tests/testHybridBayesNet.cpp

@@ -220,25 +220,24 @@ TEST(HybridBayesNet, logProbability) {
   EXPECT_LONGS_EQUAL(5, hybridBayesNet->size());
 
   HybridValues delta = hybridBayesNet->optimize();
-  auto error_tree = hybridBayesNet->logProbability(delta.continuous());
+  auto actual = hybridBayesNet->logProbability(delta.continuous());
 
   std::vector<DiscreteKey> discrete_keys = {{M(0), 2}, {M(1), 2}};
   std::vector<double> leaves = {4.1609374, 4.1706942, 4.141568, 4.1609374};
-  AlgebraicDecisionTree<Key> expected_error(discrete_keys, leaves);
+  AlgebraicDecisionTree<Key> expected(discrete_keys, leaves);
 
   // regression
-  EXPECT(assert_equal(expected_error, error_tree, 1e-6));
+  EXPECT(assert_equal(expected, actual, 1e-6));
 
   // logProbability on pruned Bayes net
   auto prunedBayesNet = hybridBayesNet->prune(2);
-  auto pruned_error_tree = prunedBayesNet.logProbability(delta.continuous());
+  auto pruned = prunedBayesNet.logProbability(delta.continuous());
 
   std::vector<double> pruned_leaves = {2e50, 4.1706942, 2e50, 4.1609374};
-  AlgebraicDecisionTree<Key> expected_pruned_error(discrete_keys,
+  AlgebraicDecisionTree<Key> expected_pruned(discrete_keys, pruned_leaves);
-                                                   pruned_leaves);
 
   // regression
-  EXPECT(assert_equal(expected_pruned_error, pruned_error_tree, 1e-6));
+  EXPECT(assert_equal(expected_pruned, pruned, 1e-6));
 
   // Verify logProbability computation and check for specific logProbability
   // value
@@ -253,9 +252,8 @@ TEST(HybridBayesNet, logProbability) {
       hybridBayesNet->at(2)->asMixture()->logProbability(hybridValues);
 
   // TODO(dellaert): the discrete errors are not added in logProbability tree!
-  EXPECT_DOUBLES_EQUAL(logProbability, error_tree(discrete_values), 1e-9);
+  EXPECT_DOUBLES_EQUAL(logProbability, actual(discrete_values), 1e-9);
-  EXPECT_DOUBLES_EQUAL(logProbability, pruned_error_tree(discrete_values),
+  EXPECT_DOUBLES_EQUAL(logProbability, pruned(discrete_values), 1e-9);
-                       1e-9);
 
   logProbability +=
       hybridBayesNet->at(3)->asDiscrete()->logProbability(discrete_values);

python/gtsam/tests/test_HybridFactorGraph.py

@@ -172,10 +172,9 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         marginals /= marginals.sum()
         return marginals
 
-    @unittest.skip
     def test_tiny(self):
         """Test a tiny two variable hybrid model."""
-        # P(x0)P(mode)P(z0|x0,mode)
+        # Create P(x0)P(mode)P(z0|x0,mode)
         prior_sigma = 0.5
         bayesNet = self.tiny(prior_sigma=prior_sigma)
 
@@ -210,9 +209,17 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         self.assertAlmostEqual(marginals[0], 0.74, delta=0.01)
         self.assertAlmostEqual(marginals[1], 0.26, delta=0.01)
 
+        # Convert to factor graph with given measurements.
         fg = bayesNet.toFactorGraph(measurements)
         self.assertEqual(fg.size(), 4)
 
+        # Check ratio between unnormalized posterior and factor graph is the same for all modes:
+        for mode in [1, 0]:
+            values.insert_or_assign(M(0), mode)
+            self.assertAlmostEqual(bayesNet.evaluate(values) /
+                                   fg.error(values),
+                                   0.025178994744461187)
+
         # Test elimination.
         posterior = fg.eliminateSequential()
 
@@ -239,6 +246,7 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         return bayesNet.evaluate(sample) / fg.probPrime(sample) if \
             fg.probPrime(sample) > 0 else 0
 
+    @unittest.skip
     def test_ratio(self):
         """
         Given a tiny two variable hybrid model, with 2 measurements, test the