Added tests for BA and MLE

src/parameter_learning.rs

@@ -114,8 +114,8 @@ impl ParameterLearning for MLE {
 }
 
 pub struct BayesianApproach {
-    default_alpha: usize,
+    pub default_alpha: usize,
-    default_tau: f64
+    pub default_tau: f64
 }
 
 impl ParameterLearning for BayesianApproach {

tests/parameter_learning.rs

@@ -15,8 +15,7 @@ use std::collections::BTreeSet;
 extern crate approx;
 
 
-#[test]
+fn learn_binary_cim<T: ParameterLearning> (pl: T) {
-fn learn_binary_cim_MLE() {
     let mut net = CtbnNetwork::init();
     let n1 = net
         .add_node(generate_discrete_time_continous_node(String::from("n1"),2))
@@ -42,8 +41,7 @@ fn learn_binary_cim_MLE() {
     }
 
     let data = trajectory_generator(&net, 100, 100.0);
-    let mle = MLE{};
+    let (CIM, M, T) = pl.fit(&net, &data, 1, None);
-    let (CIM, M, T) = mle.fit(&net, &data, 1, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
     assert_eq!(CIM.shape(), [2, 2, 2]);
     assert!(CIM.abs_diff_eq(&arr3(&[
@@ -52,9 +50,22 @@ fn learn_binary_cim_MLE() {
             ]), 0.2));
 }
 
+#[test]
+fn learn_binary_cim_MLE() {
+    let mle = MLE{};
+    learn_binary_cim(mle);
+}
+
 
 #[test]
-fn learn_ternary_cim_MLE() {
+fn learn_binary_cim_BA() {
+    let ba = BayesianApproach{
+        default_alpha: 1,
+        default_tau: 1.0};
+    learn_binary_cim(ba);
+}
+
+fn learn_ternary_cim<T: ParameterLearning> (pl: T) {
     let mut net = CtbnNetwork::init();
     let n1 = net
         .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
@@ -83,8 +94,7 @@ fn learn_ternary_cim_MLE() {
     }
 
     let data = trajectory_generator(&net, 100, 200.0);
-    let mle = MLE{};
+    let (CIM, M, T) = pl.fit(&net, &data, 1, None);
-    let (CIM, M, T) = mle.fit(&net, &data, 1, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
     assert_eq!(CIM.shape(), [3, 3, 3]);
     assert!(CIM.abs_diff_eq(&arr3(&[
@@ -94,8 +104,23 @@ fn learn_ternary_cim_MLE() {
             ]), 0.2));
 }
 
+
 #[test]
-fn learn_ternary_cim_MLE_no_parents() {
+fn learn_ternary_cim_MLE() {
+    let mle = MLE{};
+    learn_ternary_cim(mle);
+}
+
+
+#[test]
+fn learn_ternary_cim_BA() {
+    let ba = BayesianApproach{
+        default_alpha: 1,
+        default_tau: 1.0};
+    learn_ternary_cim(ba);
+}
+
+fn learn_ternary_cim_no_parents<T: ParameterLearning> (pl: T) {
     let mut net = CtbnNetwork::init();
     let n1 = net
         .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
@@ -124,11 +149,115 @@ fn learn_ternary_cim_MLE_no_parents() {
     }
 
     let data = trajectory_generator(&net, 100, 200.0);
-    let mle = MLE{};
+    let (CIM, M, T) = pl.fit(&net, &data, 0, None);
-    let (CIM, M, T) = mle.fit(&net, &data, 0, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
     assert_eq!(CIM.shape(), [1, 3, 3]);
     assert!(CIM.abs_diff_eq(&arr3(&[[[-3.0, 2.0, 1.0], 
                                   [1.5, -2.0, 0.5],
                                   [0.4, 0.6, -1.0]]]), 0.2));
 }
+
+
+#[test]
+fn learn_ternary_cim_no_parents_MLE() {
+    let mle = MLE{};
+    learn_ternary_cim_no_parents(mle);
+}
+
+
+#[test]
+fn learn_ternary_cim_no_parents_BA() {
+    let ba = BayesianApproach{
+        default_alpha: 1,
+        default_tau: 1.0};
+    learn_ternary_cim_no_parents(ba);
+}
+
+
+fn learn_mixed_discrete_cim<T: ParameterLearning> (pl: 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) => {
+            param.cim = Some(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) => {
+            param.cim = Some(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) => {
+            param.cim = Some(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, 200.0);
+    let (CIM, M, T) = pl.fit(&net, &data, 2, None);
+    print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
+    assert_eq!(CIM.shape(), [9, 4, 4]);
+    assert!(CIM.abs_diff_eq(&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]],
+            ]), 0.2));
+}
+
+#[test]
+fn learn_mixed_discrete_cim_MLE() {
+    let mle = MLE{};
+    learn_mixed_discrete_cim(mle);
+}
+
+
+#[test]
+fn learn_mixed_discrete_cim_BA() {
+    let ba = BayesianApproach{
+        default_alpha: 1,
+        default_tau: 1.0};
+    learn_mixed_discrete_cim(ba);
+}