update python tests

borglab · Sep 17, 2024 · c1ebdb2 · c1ebdb2
1 parent dbd0ae1
commit c1ebdb2
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Showing 2 changed files with 7 additions and 13 deletions.
diff --git a/python/gtsam/tests/test_HybridFactorGraph.py b/python/gtsam/tests/test_HybridFactorGraph.py
@@ -20,7 +20,7 @@
 from gtsam import (DiscreteConditional, DiscreteKeys, GaussianConditional,
                    HybridBayesNet, HybridGaussianConditional,
                    HybridGaussianFactor, HybridGaussianFactorGraph,
-                   HybridValues, JacobianFactor, Ordering, noiseModel)
+                   HybridValues, JacobianFactor, noiseModel)
 
 DEBUG_MARGINALS = False
 
@@ -31,13 +31,11 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
     def test_create(self):
         """Test construction of hybrid factor graph."""
         model = noiseModel.Unit.Create(3)
-        dk = DiscreteKeys()
-        dk.push_back((C(0), 2))
 
         jf1 = JacobianFactor(X(0), np.eye(3), np.zeros((3, 1)), model)
         jf2 = JacobianFactor(X(0), np.eye(3), np.ones((3, 1)), model)
 
-        gmf = HybridGaussianFactor([X(0)], dk, [(jf1, 0), (jf2, 0)])
+        gmf = HybridGaussianFactor([X(0)], (C(0), 2), [(jf1, 0), (jf2, 0)])
 
         hfg = HybridGaussianFactorGraph()
         hfg.push_back(jf1)
@@ -58,13 +56,11 @@ def test_create(self):
     def test_optimize(self):
         """Test construction of hybrid factor graph."""
         model = noiseModel.Unit.Create(3)
-        dk = DiscreteKeys()
-        dk.push_back((C(0), 2))
 
         jf1 = JacobianFactor(X(0), np.eye(3), np.zeros((3, 1)), model)
         jf2 = JacobianFactor(X(0), np.eye(3), np.ones((3, 1)), model)
 
-        gmf = HybridGaussianFactor([X(0)], dk, [(jf1, 0), (jf2, 0)])
+        gmf = HybridGaussianFactor([X(0)], (C(0), 2), [(jf1, 0), (jf2, 0)])
 
         hfg = HybridGaussianFactorGraph()
         hfg.push_back(jf1)
@@ -96,8 +92,6 @@ def tiny(num_measurements: int = 1,
 
         # Create Gaussian mixture Z(0) = X(0) + noise for each measurement.
         I_1x1 = np.eye(1)
-        keys = DiscreteKeys()
-        keys.push_back(mode)
         for i in range(num_measurements):
             conditional0 = GaussianConditional.FromMeanAndStddev(Z(i),
                                                                  I_1x1,
@@ -107,8 +101,10 @@ def tiny(num_measurements: int = 1,
                                                                  I_1x1,
                                                                  X(0), [0],
                                                                  sigma=3)
+            discreteParents = DiscreteKeys()
+            discreteParents.push_back(mode)
             bayesNet.push_back(
-                HybridGaussianConditional([Z(i)], [X(0)], keys,
+                HybridGaussianConditional([Z(i)], [X(0)], discreteParents,
                                           [conditional0, conditional1]))
 
         # Create prior on X(0).

diff --git a/python/gtsam/tests/test_HybridNonlinearFactorGraph.py b/python/gtsam/tests/test_HybridNonlinearFactorGraph.py
@@ -27,8 +27,6 @@ class TestHybridGaussianFactorGraph(GtsamTestCase):
 
     def test_nonlinear_hybrid(self):
         nlfg = gtsam.HybridNonlinearFactorGraph()
-        dk = gtsam.DiscreteKeys()
-        dk.push_back((10, 2))
         nlfg.push_back(
             BetweenFactorPoint3(1, 2, Point3(1, 2, 3),
                                 noiseModel.Diagonal.Variances([1, 1, 1])))
@@ -40,7 +38,7 @@ def test_nonlinear_hybrid(self):
                                       noiseModel.Unit.Create(3)), 0.0),
                    (PriorFactorPoint3(1, Point3(1, 2, 1),
                                       noiseModel.Unit.Create(3)), 0.0)]
-        nlfg.push_back(gtsam.HybridNonlinearFactor([1], dk, factors))
+        nlfg.push_back(gtsam.HybridNonlinearFactor([1], (10, 2), factors))
         nlfg.push_back(gtsam.DecisionTreeFactor((10, 2), "1 3"))
         values = gtsam.Values()
         values.insert_point3(1, Point3(0, 0, 0))