Changed trajectory_generator to use generics instead of Box for the network object

src/tools.rs

@@ -13,8 +13,8 @@ pub struct Dataset {
     pub trajectories: Vec<Trajectory>,
 }
 
-pub fn trajectory_generator(
-    net: Box<&dyn network::Network>,
+pub fn trajectory_generator<T: network::Network>(
+    net: &T,
     n_trajectories: u64,
     t_end: f64,
 ) -> Dataset {

tests/parameter_learning.rs

@@ -41,7 +41,7 @@ fn learn_binary_cim_MLE() {
         }
     }
 
-    let data = trajectory_generator(Box::new(&net), 100, 100.0);
+    let data = trajectory_generator(&net, 100, 100.0);
     let mle = MLE{};
     let (CIM, M, T) = mle.fit(Box::new(&net), &data, 1, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
@@ -82,7 +82,7 @@ fn learn_ternary_cim_MLE() {
         }
     }
 
-    let data = trajectory_generator(Box::new(&net), 100, 200.0);
+    let data = trajectory_generator(&net, 100, 200.0);
     let mle = MLE{};
     let (CIM, M, T) = mle.fit(Box::new(&net), &data, 1, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
@@ -123,7 +123,7 @@ fn learn_ternary_cim_MLE_no_parents() {
         }
     }
 
-    let data = trajectory_generator(Box::new(&net), 100, 200.0);
+    let data = trajectory_generator(&net, 100, 200.0);
     let mle = MLE{};
     let (CIM, M, T) = mle.fit(Box::new(&net), &data, 0, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);

tests/tools.rs

@@ -36,7 +36,7 @@ fn run_sampling() {
         }
     }
 
-    let data = trajectory_generator(Box::new(&net), 4, 1.0);
+    let data = trajectory_generator(&net, 4, 1.0);
 
     assert_eq!(4, data.trajectories.len());
     assert_relative_eq!(1.0, data.trajectories[0].time[data.trajectories[0].time.len()-1]);