Formatting

src/lib.rs

@@ -6,6 +6,6 @@ pub mod ctbn;
 pub mod network;
 pub mod parameter_learning;
 pub mod params;
+pub mod sampling;
 pub mod structure_learning;
 pub mod tools;
-pub mod sampling;

src/sampling.rs

@@ -1,5 +1,5 @@
 use crate::{
-    network::{Network},
+    network::Network,
     params::{self, ParamsTrait},
 };
 use rand::SeedableRng;
@@ -65,7 +65,8 @@ impl<'a, T: Network> Iterator for ForwardSampler<'a, T> {
             }
         }
 
-        let next_node_transition = self.next_transitions
+        let next_node_transition = self
+            .next_transitions
             .iter()
             .enumerate()
             .min_by(|x, y| x.1.unwrap().partial_cmp(&y.1.unwrap()).unwrap())
@@ -74,12 +75,15 @@ impl<'a, T: Network> Iterator for ForwardSampler<'a, T> {
 
         self.current_time = self.next_transitions[next_node_transition].unwrap().clone();
 
-        self.current_state[next_node_transition] = self.net
+        self.current_state[next_node_transition] = self
+            .net
             .get_node(next_node_transition)
             .get_random_state(
-                self.net.get_node(next_node_transition)
+                self.net
+                    .get_node(next_node_transition)
                     .state_to_index(&self.current_state[next_node_transition]),
-                self.net.get_param_index_network(next_node_transition, &self.current_state),
+                self.net
+                    .get_param_index_network(next_node_transition, &self.current_state),
                 &mut self.rng,
             )
             .unwrap();
@@ -90,7 +94,6 @@ impl<'a, T: Network> Iterator for ForwardSampler<'a, T> {
             self.next_transitions[child] = None;
         }
 
-
         Some((ret_time, ret_state))
     }
 }

src/tools.rs

@@ -1,6 +1,6 @@
 use ndarray::prelude::*;
 
-use crate::sampling::{Sampler, ForwardSampler};
+use crate::sampling::{ForwardSampler, Sampler};
 use crate::{network, params};
 
 pub struct Trajectory {