Address review comments

gtsam/hybrid/HybridLookupDAG.cpp

@@ -55,8 +55,7 @@ void HybridLookupTable::argmaxInPlace(HybridValues* values) const {
   }
 }
 
-// /* **************************************************************************
-// */
+/* ************************************************************************** */
 HybridLookupDAG HybridLookupDAG::FromBayesNet(const HybridBayesNet& bayesNet) {
   HybridLookupDAG dag;
   for (auto&& conditional : bayesNet) {
@@ -66,12 +65,12 @@ HybridLookupDAG HybridLookupDAG::FromBayesNet(const HybridBayesNet& bayesNet) {
   return dag;
 }
 
+/* ************************************************************************** */
 HybridValues HybridLookupDAG::argmax(HybridValues result) const {
   // Argmax each node in turn in topological sort order (parents first).
   for (auto lookupTable : boost::adaptors::reverse(*this))
     lookupTable->argmaxInPlace(&result);
   return result;
 }
-/* ************************************************************************** */
 
-}  // namespace gtsam
+}  // namespace gtsam

python/gtsam/tests/test_HybridFactorGraph.py

@@ -55,6 +55,37 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
         discrete_conditional = hbn.at(hbn.size() - 1).inner()
         self.assertIsInstance(discrete_conditional, gtsam.DiscreteConditional)
 
+    def test_optimize(self):
+        """Test contruction of hybrid factor graph."""
+        noiseModel = gtsam.noiseModel.Unit.Create(3)
+        dk = gtsam.DiscreteKeys()
+        dk.push_back((C(0), 2))
+
+        jf1 = gtsam.JacobianFactor(X(0), np.eye(3), np.zeros((3, 1)),
+                                   noiseModel)
+        jf2 = gtsam.JacobianFactor(X(0), np.eye(3), np.ones((3, 1)),
+                                   noiseModel)
+
+        gmf = gtsam.GaussianMixtureFactor.FromFactors([X(0)], dk, [jf1, jf2])
+
+        hfg = gtsam.HybridGaussianFactorGraph()
+        hfg.add(jf1)
+        hfg.add(jf2)
+        hfg.push_back(gmf)
+
+        dtf = gtsam.DecisionTreeFactor([(C(0), 2)],"0 1")
+        hfg.add(dtf)
+
+        hbn = hfg.eliminateSequential(
+            gtsam.Ordering.ColamdConstrainedLastHybridGaussianFactorGraph(
+                hfg, [C(0)]))
+
+        # print("hbn = ", hbn)
+        hv = hbn.optimize()
+        self.assertEqual(hv.atDiscrete(C(0)), 1)
+
+        self.assertEqual(hv.at(X(0)), np.ones((3, 1)))
+
 
 if __name__ == "__main__":
     unittest.main()

python/gtsam/tests/test_HybridValues.py

@@ -0,0 +1,41 @@
+"""
+GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
+Atlanta, Georgia 30332-0415
+All Rights Reserved
+
+See LICENSE for the license information
+
+Unit tests for Hybrid Values.
+Author: Shangjie Xue
+"""
+# pylint: disable=invalid-name, no-name-in-module, no-member
+
+from __future__ import print_function
+
+import unittest
+
+import gtsam
+import numpy as np
+from gtsam.symbol_shorthand import C, X
+from gtsam.utils.test_case import GtsamTestCase
+
+
+class TestHybridGaussianFactorGraph(GtsamTestCase):
+    """Unit tests for HybridValues."""
+
+    def test_basic(self):
+        """Test contruction and basic methods of hybrid values."""
+        
+        hv1 = gtsam.HybridValues()
+        hv1.insert(X(0), np.ones((3,1)))
+        hv1.insert(C(0), 2)
+
+        hv2 = gtsam.HybridValues()
+        hv2.insert(C(0), 2)
+        hv2.insert(X(0), np.ones((3,1)))
+
+        self.assertEqual(hv1.atDiscrete(C(0)), 2)
+        self.assertEqual(hv1.at(X(0))[0], np.ones((3,1))[0])
+
+if __name__ == "__main__":
+    unittest.main()