Refactor

src/structure_learning.rs

@@ -4,7 +4,7 @@ use crate::network;
 use crate::tools;
 
 pub trait StructureLearningAlgorithm {
-    fn fit<T, >(&self, net: T, dataset: &tools::Dataset) -> T
+    fn fit_transform<T, >(&self, net: T, dataset: &tools::Dataset) -> T
     where
         T: network::Network;
 }

src/structure_learning/score_based_algorithm.rs

@@ -1,11 +1,7 @@
-use crate::params;
 use crate::structure_learning::score_function::ScoreFunction;
 use crate::structure_learning::StructureLearningAlgorithm;
 use crate::tools;
-use crate::{network, parameter_learning};
-use ndarray::prelude::*;
-use rand::prelude::*;
-use rand_chacha::ChaCha8Rng;
+use crate::network;
 use std::collections::BTreeSet;
 
 pub struct HillClimbing<S: ScoreFunction> {
@@ -19,7 +15,7 @@ impl<S: ScoreFunction> HillClimbing<S> {
 }
 
 impl<S: ScoreFunction> StructureLearningAlgorithm for HillClimbing<S> {
-    fn fit<T>(&self, net: T, dataset: &tools::Dataset) -> T
+    fn fit_transform<T>(&self, net: T, dataset: &tools::Dataset) -> T
     where
         T: network::Network,
     {

tests/structure_learning.rs

@@ -87,7 +87,7 @@ fn learn_ternary_net_2_nodes<T: StructureLearningAlgorithm> (sl: T) {
 
     let data = trajectory_generator(&net, 100, 20.0, Some(6347747169756259),);
 
-    let net = sl.fit(net, &data);
+    let net = sl.fit_transform(net, &data);
     assert_eq!(BTreeSet::from_iter(vec![n1]), net.get_parent_set(n2));
     assert_eq!(BTreeSet::new(), net.get_parent_set(n1));
 }
@@ -164,7 +164,7 @@ fn learn_mixed_discrete_net_3_nodes<T: StructureLearningAlgorithm> (sl: T) {
 
 
     let data = trajectory_generator(&net, 300, 30.0, Some(6347747169756259),);
-    let net = sl.fit(net, &data);
+    let net = sl.fit_transform(net, &data);
 
     assert_eq!(BTreeSet::new(), net.get_parent_set(n1));
     assert_eq!(BTreeSet::from_iter(vec![n1]), net.get_parent_set(n2));