diff --git a/tests/structure_learning.rs b/tests/structure_learning.rs
index 2ddd513..e0e6d9b 100644
--- a/tests/structure_learning.rs
+++ b/tests/structure_learning.rs
@@ -85,7 +85,7 @@ fn learn_ternary_net_2_nodes<T: StructureLearningAlgorithm> (sl: T) {
         }
     }
 
-    let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259),);
+    let data = trajectory_generator(&net, 100, 20.0, Some(6347747169756259),);
 
     let net = sl.call(net, &data);
     assert_eq!(BTreeSet::from_iter(vec![n1]), net.get_parent_set(n2));
@@ -106,3 +106,82 @@ pub fn learn_ternary_net_2_nodes_hill_climbing_bic() {
     let hl = HillClimbing::init(bic);
     learn_ternary_net_2_nodes(hl);
 }
+
+
+
+fn learn_mixed_discrete_net_3_nodes<T: StructureLearningAlgorithm> (sl: T) {
+    let mut net = CtbnNetwork::init();
+    let n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
+        .unwrap();
+    let n2 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n2"),3))
+        .unwrap();
+
+    let n3 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n3"),4))
+        .unwrap();
+    net.add_edge(n1, n2);
+    net.add_edge(n1, n3);
+    net.add_edge(n2, n3);
+
+    match &mut net.get_node_mut(n1).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0], 
+                                  [1.5, -2.0, 0.5],
+                                  [0.4, 0.6, -1.0]]])));
+        }
+    }
+
+    match &mut net.get_node_mut(n2).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            assert_eq!(Ok(()), param.set_cim(arr3(&[
+                [[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]],
+                [[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]],
+                [[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]],
+            ])));
+        }
+    }
+
+
+    match &mut net.get_node_mut(n3).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            assert_eq!(Ok(()), param.set_cim(arr3(&[
+                [[-1.0, 0.5, 0.3, 0.2], [0.5, -4.0, 2.5, 1.0], [2.5, 0.5, -4.0, 1.0], [0.7, 0.2, 0.1, -1.0]],
+                [[-6.0, 2.0, 3.0, 1.0], [1.5, -3.0, 0.5, 1.0], [2.0, 1.3, -5.0 ,1.7], [2.5, 0.5, 1.0, -4.0]],
+                [[-1.3, 0.3, 0.1, 0.9], [1.4, -4.0, 0.5, 2.1], [1.0, 1.5, -3.0, 0.5], [0.4, 0.3, 0.1, -0.8]],
+
+                [[-2.0, 1.0, 0.7, 0.3], [1.3, -5.9, 2.7, 1.9], [2.0, 1.5, -4.0, 0.5], [0.2, 0.7, 0.1, -1.0]],
+                [[-6.0, 1.0, 2.0, 3.0], [0.5, -3.0, 1.0, 1.5], [1.4, 2.1, -4.3, 0.8], [0.5, 1.0, 2.5, -4.0]],
+                [[-1.3, 0.9, 0.3, 0.1], [0.1, -1.3, 0.2, 1.0], [0.5, 1.0, -3.0, 1.5], [0.1, 0.4, 0.3, -0.8]],
+
+                [[-2.0, 1.0, 0.6, 0.4], [2.6, -7.1, 1.4, 3.1], [5.0, 1.0, -8.0, 2.0], [1.4, 0.4, 0.2, -2.0]],
+                [[-3.0, 1.0, 1.5, 0.5], [3.0, -6.0, 1.0, 2.0], [0.3, 0.5, -1.9, 1.1], [5.0, 1.0, 2.0, -8.0]],
+                [[-2.6, 0.6, 0.2, 1.8], [2.0, -6.0, 3.0, 1.0], [0.1, 0.5, -1.3, 0.7], [0.8, 0.6, 0.2, -1.6]],
+            ])));
+        }
+    }
+
+
+    let data = trajectory_generator(&net, 300, 30.0, Some(6347747169756259),);
+    let net = sl.call(net, &data);
+
+    assert_eq!(BTreeSet::new(), net.get_parent_set(n1));
+    assert_eq!(BTreeSet::from_iter(vec![n1]), net.get_parent_set(n2));
+    assert_eq!(BTreeSet::from_iter(vec![n1, n2]), net.get_parent_set(n3));
+}
+
+
+#[test]
+pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_ll() {
+    let ll = LogLikelihood::init(1, 1.0);
+    let hl = HillClimbing::init(ll);
+    learn_mixed_discrete_net_3_nodes(hl);
+}
+
+#[test]
+pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic() {
+    let bic = BIC::init(1, 1.0);
+    let hl = HillClimbing::init(bic);
+    learn_mixed_discrete_net_3_nodes(hl);
+}