Changed parameter_learning module to use generics instead of Box for the network object

src/parameter_learning.rs

@@ -6,17 +6,17 @@ use ndarray::{concatenate, Slice};
 use std::collections::BTreeSet;
 
 pub trait ParameterLearning{
-    fn fit(
+    fn fit<T:network::Network>(
         &self,
-        net: Box<&dyn network::Network>,
+        net: &T,
         dataset: &tools::Dataset,
         node: usize,
         parent_set: Option<BTreeSet<usize>>,
     ) -> (Array3<f64>, Array3<usize>, Array2<f64>);
 }
 
-pub fn sufficient_statistics(
+pub fn sufficient_statistics<T:network::Network>(
-    net: Box<&dyn network::Network>,
+    net: &T,
     dataset: &tools::Dataset,
     node: usize,
     parent_set: &BTreeSet<usize>
@@ -80,9 +80,9 @@ pub struct MLE {}
 
 impl ParameterLearning for MLE {
 
-    fn fit(
+    fn fit<T: network::Network>(
         &self,
-        net: Box<&dyn network::Network>,
+        net: &T,
         dataset: &tools::Dataset,
         node: usize,
         parent_set: Option<BTreeSet<usize>>,
@@ -119,9 +119,9 @@ pub struct BayesianApproach {
 }
 
 impl ParameterLearning for BayesianApproach {
-    fn fit(
+    fn fit<T: network::Network>(
         &self,
-        net: Box<&dyn network::Network>,
+        net: &T,
         dataset: &tools::Dataset,
         node: usize,
         parent_set: Option<BTreeSet<usize>>,

tests/parameter_learning.rs

@@ -43,7 +43,7 @@ fn learn_binary_cim_MLE() {
 
     let data = trajectory_generator(&net, 100, 100.0);
     let mle = MLE{};
-    let (CIM, M, T) = mle.fit(Box::new(&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(&[
@@ -84,7 +84,7 @@ fn learn_ternary_cim_MLE() {
 
     let data = trajectory_generator(&net, 100, 200.0);
     let mle = MLE{};
-    let (CIM, M, T) = mle.fit(Box::new(&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(&[
@@ -125,7 +125,7 @@ fn learn_ternary_cim_MLE_no_parents() {
 
     let data = trajectory_generator(&net, 100, 200.0);
     let mle = MLE{};
-    let (CIM, M, T) = mle.fit(Box::new(&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],