fix python tests

Author: varunagrawal

python/gtsam/tests/test_HybridFactorGraph.py

@@ -37,7 +37,7 @@ def test_create(self):
         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, jf2])
+        gmf = HybridGaussianFactor([X(0)], dk, [(jf1, 0), (jf2, 0)])
 
         hfg = HybridGaussianFactorGraph()
         hfg.push_back(jf1)
@@ -64,7 +64,7 @@ def test_optimize(self):
         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, jf2])
+        gmf = HybridGaussianFactor([X(0)], dk, [(jf1, 0), (jf2, 0)])
 
         hfg = HybridGaussianFactorGraph()
         hfg.push_back(jf1)

python/gtsam/tests/test_HybridNonlinearFactorGraph.py

@@ -35,13 +35,12 @@ def test_nonlinear_hybrid(self):
         nlfg.push_back(
             PriorFactorPoint3(2, Point3(1, 2, 3),
                               noiseModel.Diagonal.Variances([0.5, 0.5, 0.5])))
-        nlfg.push_back(
-            gtsam.HybridNonlinearFactor([1], dk, [
-                PriorFactorPoint3(1, Point3(0, 0, 0),
-                                  noiseModel.Unit.Create(3)),
-                PriorFactorPoint3(1, Point3(1, 2, 1),
-                                  noiseModel.Unit.Create(3))
-            ]))
+
+        factors = [(PriorFactorPoint3(1, Point3(0, 0, 0),
+                                      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.DecisionTreeFactor((10, 2), "1 3"))
         values = gtsam.Values()
         values.insert_point3(1, Point3(0, 0, 0))