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,
@@ -66,34 +76,39 @@ pub fn sufficient_statistics(
 
 }
 
-pub fn MLE(
-    net: Box<&dyn network::Network>,
-    dataset: &tools::Dataset,
-    node: usize,
-    parent_set: Option<BTreeSet<usize>>,
-) -> (Array3<f64>, Array3<usize>, Array2<f64>) {
-    //TODO: make this function general. Now it works only on ContinousTimeDiscreteState nodes
-
-    //Use parent_set from parameter if present. Otherwise use parent_set from network.
-    let parent_set = match parent_set {
-        Some(p) => p,
-        None => net.get_parent_set(node),
-    };
-    
-    let (M, T) = sufficient_statistics(net, dataset, node.clone(), &parent_set);
-    //Compute the CIM as M[i,x,y]/T[i,x]  
-    let mut CIM: Array3<f64> = Array::zeros((M.shape()[0], M.shape()[1], M.shape()[2]));
-    CIM.axis_iter_mut(Axis(2))
-        .zip(M.mapv(|x| x as f64).axis_iter(Axis(2)))
-        .for_each(|(mut C, m)| C.assign(&(&m/&T)));
-
-    //Set the diagonal of the inner matrices to the the row sum multiplied by -1
-    let tmp_diag_sum: Array2<f64> = CIM.sum_axis(Axis(2)).mapv(|x| x * -1.0);
-    CIM.outer_iter_mut()
-        .zip(tmp_diag_sum.outer_iter())
-        .for_each(|(mut C, diag)| {
-            C.diag_mut().assign(&diag);
-        });
-    return (CIM, M, T);
-}
+pub struct MLE {}
 
+impl ParameterLearning for MLE {
+
+    fn fit(
+        &self,
+        net: Box<&dyn network::Network>,
+        dataset: &tools::Dataset,
+        node: usize,
+        parent_set: Option<BTreeSet<usize>>,
+    ) -> (Array3<f64>, Array3<usize>, Array2<f64>) {
+        //TODO: make this function general. Now it works only on ContinousTimeDiscreteState nodes
+
+        //Use parent_set from parameter if present. Otherwise use parent_set from network.
+        let parent_set = match parent_set {
+            Some(p) => p,
+            None => net.get_parent_set(node),
+        };
+        
+        let (M, T) = sufficient_statistics(net, dataset, node.clone(), &parent_set);
+        //Compute the CIM as M[i,x,y]/T[i,x]  
+        let mut CIM: Array3<f64> = Array::zeros((M.shape()[0], M.shape()[1], M.shape()[2]));
+        CIM.axis_iter_mut(Axis(2))
+            .zip(M.mapv(|x| x as f64).axis_iter(Axis(2)))
+            .for_each(|(mut C, m)| C.assign(&(&m/&T)));
+
+        //Set the diagonal of the inner matrices to the the row sum multiplied by -1
+        let tmp_diag_sum: Array2<f64> = CIM.sum_axis(Axis(2)).mapv(|x| x * -1.0);
+        CIM.outer_iter_mut()
+            .zip(tmp_diag_sum.outer_iter())
+            .for_each(|(mut C, diag)| {
+                C.diag_mut().assign(&diag);
+            });
+        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 (CIM, M, T) = MLE(Box::new(&net), &data, 1, None);
+    let mle = MLE{};
+    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 (CIM, M, T) = MLE(Box::new(&net), &data, 1, None);
+    let mle = MLE{};
+    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);