parameter_learning refactor

src/parameter_learning.rs

@@ -5,6 +5,16 @@ use ndarray::prelude::*;
 use ndarray::{concatenate, Slice};
 use std::collections::BTreeSet;
 
+pub trait ParameterLearning{
+    fn fit(
+        &self,
+        net: Box<&dyn network::Network>,
+        dataset: &tools::Dataset,
+        node: usize,
+        parent_set: Option<BTreeSet<usize>>,
+    ) -> (Array3<f64>, Array3<usize>, Array2<f64>);
+}
+
 pub fn sufficient_statistics(
     net: Box<&dyn network::Network>,
     dataset: &tools::Dataset,
@@ -66,7 +76,12 @@ pub fn sufficient_statistics(
 
 }
 
-pub fn MLE(
+pub struct MLE {}
+
+impl ParameterLearning for MLE {
+
+    fn fit(
+        &self,
         net: Box<&dyn network::Network>,
         dataset: &tools::Dataset,
         node: usize,
@@ -96,4 +111,4 @@ pub fn MLE(
             });
         return (CIM, M, T);
     }
-
+}

tests/parameter_learning.rs

@@ -42,8 +42,8 @@ fn learn_binary_cim_MLE() {
     }
 
     let data = trajectory_generator(Box::new(&net), 100, 100.0);
-
+    let mle = MLE{};
-    let (CIM, M, T) = MLE(Box::new(&net), &data, 1, None);
+    let (CIM, M, T) = mle.fit(Box::new(&net), &data, 1, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
     assert_eq!(CIM.shape(), [2, 2, 2]);
     assert_relative_eq!(-1.0, CIM[[0, 0, 0]], epsilon=0.2);
@@ -83,8 +83,8 @@ fn learn_ternary_cim_MLE() {
     }
 
     let data = trajectory_generator(Box::new(&net), 100, 200.0);
-
+    let mle = MLE{};
-    let (CIM, M, T) = MLE(Box::new(&net), &data, 1, None);
+    let (CIM, M, T) = mle.fit(Box::new(&net), &data, 1, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
     assert_eq!(CIM.shape(), [3, 3, 3]);
     assert_relative_eq!(-1.0, CIM[[0, 0, 0]], epsilon=0.2);