WIP: Added tests for CTPC

reCTBN/tests/structure_learning.rs

@@ -10,6 +10,7 @@ use reCTBN::parameter_learning::BayesianApproach;
 use reCTBN::parameter_learning::Cache;
 use reCTBN::params;
 use reCTBN::structure_learning::hypothesis_test::*;
+use reCTBN::structure_learning::constraint_based_algorithm::*;
 use reCTBN::structure_learning::score_based_algorithm::*;
 use reCTBN::structure_learning::score_function::*;
 use reCTBN::structure_learning::StructureLearningAlgorithm;
@@ -497,3 +498,81 @@ pub fn f_call() {
     separation_set.insert(N1);
     assert!(f.call(&net, N2, N3, &separation_set, &mut cache));
 }
+
+#[test]
+pub fn learn_ternary_net_2_nodes_ctpc() {
+    let mut net = CtbnNetwork::new();
+    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();
+    net.add_edge(n1, n2);
+
+    match &mut net.get_node_mut(n1) {
+        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::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]
+                    ],
+                ]))
+            );
+        }
+    }
+
+    let data = trajectory_generator(&net, 100, 20.0, Some(6347747169756259));
+
+    let f = F::new(0.000001);
+    let chi_sq = ChiSquare::new(0.0001);
+    let parameter_learning = BayesianApproach { alpha: 1, tau:1.0 };
+    let cache = Cache::new(parameter_learning, data.clone());
+    let mut ctpc = CTPC::new(f, chi_sq, cache);
+
+
+    let net = ctpc.fit_transform(net, &data);
+    assert_eq!(BTreeSet::from_iter(vec![n1]), net.get_parent_set(n2));
+    assert_eq!(BTreeSet::new(), net.get_parent_set(n1));
+}
+
+#[test]
+fn learn_mixed_discrete_net_3_nodes_ctpc() {
+    let (_, data) = get_mixed_discrete_net_3_nodes_with_data();
+
+    let f = F::new(1e-24);
+    let chi_sq = ChiSquare::new(1e-24);
+    let parameter_learning = BayesianApproach { alpha: 1, tau:1.0 };
+    let cache = Cache::new(parameter_learning, data);
+    let ctpc = CTPC::new(f, chi_sq, cache);
+
+    learn_mixed_discrete_net_3_nodes(ctpc);
+}