Added tests for the learning of parameters using uniform graph and parameters generators as complementary to their handcrafted version

reCTBN/tests/parameter_learning.rs

@@ -6,6 +6,7 @@ use reCTBN::process::ctbn::*;
 use reCTBN::process::NetworkProcess;
 use reCTBN::parameter_learning::*;
 use reCTBN::params;
+use reCTBN::params::Params::DiscreteStatesContinousTime;
 use reCTBN::tools::*;
 use utils::*;
 
@@ -66,18 +67,78 @@ fn learn_binary_cim<T: ParameterLearning>(pl: T) {
     ));
 }
 
+fn generate_nodes(
+    net: &mut CtbnNetwork,
+    nodes_cardinality: usize,
+    nodes_domain_cardinality: usize
+) {
+    for node_label in 0..nodes_cardinality {
+        net.add_node(
+            generate_discrete_time_continous_node(
+                node_label.to_string(),
+                nodes_domain_cardinality,
+            )
+        ).unwrap();
+    }
+}
+
+fn learn_binary_cim_gen<T: ParameterLearning>(pl: T) {
+    let mut net = CtbnNetwork::new();
+    generate_nodes(&mut net, 2, 2);
+
+    net.add_edge(0, 1);
+
+    let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
+        1.0..6.0,
+        Some(6813071588535822)
+    );
+    cim_generator.generate_parameters(&mut net);
+
+    let p_gen = match net.get_node(1) {
+        DiscreteStatesContinousTime(p_gen) => p_gen,
+    };
+
+    let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259));
+    let p_tj = match pl.fit(&net, &data, 1, None) {
+        DiscreteStatesContinousTime(p_tj) => p_tj,
+    };
+
+    assert_eq!(
+        p_tj.get_cim().as_ref().unwrap().shape(),
+        p_gen.get_cim().as_ref().unwrap().shape()
+    );
+    assert!(
+        p_tj.get_cim().as_ref().unwrap().abs_diff_eq(
+            &p_gen.get_cim().as_ref().unwrap(),
+            0.1
+        )
+    );
+}
+
 #[test]
 fn learn_binary_cim_MLE() {
     let mle = MLE {};
     learn_binary_cim(mle);
 }
 
+#[test]
+fn learn_binary_cim_MLE_gen() {
+    let mle = MLE {};
+    learn_binary_cim_gen(mle);
+}
+
 #[test]
 fn learn_binary_cim_BA() {
     let ba = BayesianApproach { alpha: 1, tau: 1.0 };
     learn_binary_cim(ba);
 }
 
+#[test]
+fn learn_binary_cim_BA_gen() {
+    let ba = BayesianApproach { alpha: 1, tau: 1.0 };
+    learn_binary_cim_gen(ba);
+}
+
 fn learn_ternary_cim<T: ParameterLearning>(pl: T) {
     let mut net = CtbnNetwork::new();
     let n1 = net
@@ -155,18 +216,63 @@ fn learn_ternary_cim<T: ParameterLearning>(pl: T) {
     ));
 }
 
+fn learn_ternary_cim_gen<T: ParameterLearning>(pl: T) {
+    let mut net = CtbnNetwork::new();
+    generate_nodes(&mut net, 2, 3);
+
+    net.add_edge(0, 1);
+
+    let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
+        4.0..6.0,
+        Some(6813071588535822)
+    );
+    cim_generator.generate_parameters(&mut net);
+
+    let p_gen = match net.get_node(1) {
+        DiscreteStatesContinousTime(p_gen) => p_gen,
+    };
+
+    let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
+    let p_tj = match pl.fit(&net, &data, 1, None) {
+        DiscreteStatesContinousTime(p_tj) => p_tj,
+    };
+
+    assert_eq!(
+        p_tj.get_cim().as_ref().unwrap().shape(),
+        p_gen.get_cim().as_ref().unwrap().shape()
+    );
+    assert!(
+        p_tj.get_cim().as_ref().unwrap().abs_diff_eq(
+            &p_gen.get_cim().as_ref().unwrap(),
+            0.1
+        )
+    );
+}
+
 #[test]
 fn learn_ternary_cim_MLE() {
     let mle = MLE {};
     learn_ternary_cim(mle);
 }
 
+#[test]
+fn learn_ternary_cim_MLE_gen() {
+    let mle = MLE {};
+    learn_ternary_cim_gen(mle);
+}
+
 #[test]
 fn learn_ternary_cim_BA() {
     let ba = BayesianApproach { alpha: 1, tau: 1.0 };
     learn_ternary_cim(ba);
 }
 
+#[test]
+fn learn_ternary_cim_BA_gen() {
+    let ba = BayesianApproach { alpha: 1, tau: 1.0 };
+    learn_ternary_cim_gen(ba);
+}
+
 fn learn_ternary_cim_no_parents<T: ParameterLearning>(pl: T) {
     let mut net = CtbnNetwork::new();
     let n1 = net
@@ -234,18 +340,63 @@ fn learn_ternary_cim_no_parents<T: ParameterLearning>(pl: T) {
     ));
 }
 
+fn learn_ternary_cim_no_parents_gen<T: ParameterLearning>(pl: T) {
+    let mut net = CtbnNetwork::new();
+    generate_nodes(&mut net, 2, 3);
+
+    net.add_edge(0, 1);
+
+    let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
+        1.0..6.0,
+        Some(6813071588535822)
+    );
+    cim_generator.generate_parameters(&mut net);
+
+    let p_gen = match net.get_node(0) {
+        DiscreteStatesContinousTime(p_gen) => p_gen,
+    };
+
+    let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
+    let p_tj = match pl.fit(&net, &data, 0, None) {
+        DiscreteStatesContinousTime(p_tj) => p_tj,
+    };
+
+    assert_eq!(
+        p_tj.get_cim().as_ref().unwrap().shape(),
+        p_gen.get_cim().as_ref().unwrap().shape()
+    );
+    assert!(
+        p_tj.get_cim().as_ref().unwrap().abs_diff_eq(
+            &p_gen.get_cim().as_ref().unwrap(),
+            0.1
+        )
+    );
+}
+
 #[test]
 fn learn_ternary_cim_no_parents_MLE() {
     let mle = MLE {};
     learn_ternary_cim_no_parents(mle);
 }
 
+#[test]
+fn learn_ternary_cim_no_parents_MLE_gen() {
+    let mle = MLE {};
+    learn_ternary_cim_no_parents_gen(mle);
+}
+
 #[test]
 fn learn_ternary_cim_no_parents_BA() {
     let ba = BayesianApproach { alpha: 1, tau: 1.0 };
     learn_ternary_cim_no_parents(ba);
 }
 
+#[test]
+fn learn_ternary_cim_no_parents_BA_gen() {
+    let ba = BayesianApproach { alpha: 1, tau: 1.0 };
+    learn_ternary_cim_no_parents_gen(ba);
+}
+
 fn learn_mixed_discrete_cim<T: ParameterLearning>(pl: T) {
     let mut net = CtbnNetwork::new();
     let n1 = net
@@ -432,14 +583,66 @@ fn learn_mixed_discrete_cim<T: ParameterLearning>(pl: T) {
     ));
 }
 
+fn learn_mixed_discrete_cim_gen<T: ParameterLearning>(pl: T) {
+    let mut net = CtbnNetwork::new();
+    generate_nodes(&mut net, 2, 3);
+    net.add_node(
+        generate_discrete_time_continous_node(
+            String::from("3"),
+            4
+        )
+    ).unwrap();
+    net.add_edge(0, 1);
+    net.add_edge(0, 2);
+    net.add_edge(1, 2);
+
+    let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
+        1.0..8.0,
+        Some(6813071588535822)
+    );
+    cim_generator.generate_parameters(&mut net);
+
+    let p_gen = match net.get_node(2) {
+        DiscreteStatesContinousTime(p_gen) => p_gen,
+    };
+
+    let data = trajectory_generator(&net, 300, 300.0, Some(6347747169756259));
+    let p_tj = match pl.fit(&net, &data, 2, None) {
+        DiscreteStatesContinousTime(p_tj) => p_tj,
+    };
+
+    assert_eq!(
+        p_tj.get_cim().as_ref().unwrap().shape(),
+        p_gen.get_cim().as_ref().unwrap().shape()
+    );
+    assert!(
+        p_tj.get_cim().as_ref().unwrap().abs_diff_eq(
+            &p_gen.get_cim().as_ref().unwrap(),
+            0.2
+        )
+    );
+}
+
 #[test]
 fn learn_mixed_discrete_cim_MLE() {
     let mle = MLE {};
     learn_mixed_discrete_cim(mle);
 }
 
+#[test]
+fn learn_mixed_discrete_cim_MLE_gen() {
+    let mle = MLE {};
+    learn_mixed_discrete_cim_gen(mle);
+}
+
 #[test]
 fn learn_mixed_discrete_cim_BA() {
     let ba = BayesianApproach { alpha: 1, tau: 1.0 };
     learn_mixed_discrete_cim(ba);
 }
+
+#[test]
+fn learn_mixed_discrete_cim_BA_gen() {
+    let ba = BayesianApproach { alpha: 1, tau: 1.0 };
+    learn_mixed_discrete_cim_gen(ba);
+}