Implemented basic cache

reCTBN/src/parameter_learning.rs

@@ -1,6 +1,6 @@
 //! Module containing methods used to learn the parameters.
 
-use std::collections::BTreeSet;
+use std::collections::{BTreeSet,HashMap};
 
 use ndarray::prelude::*;
 
@@ -165,13 +165,15 @@ impl ParameterLearning for BayesianApproach {
     }
 }
 
+// TODO: Move to constraint_based_algorithm.rs
 pub struct Cache<'a, P: ParameterLearning> {
     parameter_learning: &'a P,
+    cache_persistent: HashMap<Option<BTreeSet<usize>>, Params>,
 }
 
 impl<'a, P: ParameterLearning> Cache<'a, P> {
     pub fn new(parameter_learning: &'a P) -> Cache<'a, P> {
-        Cache { parameter_learning }
+        Cache { parameter_learning, cache_persistent: HashMap::new() }
     }
     pub fn fit<T: process::NetworkProcess>(
         &mut self,
@@ -180,6 +182,15 @@ impl<'a, P: ParameterLearning> Cache<'a, P> {
         node: usize,
         parent_set: Option<BTreeSet<usize>>,
     ) -> Params {
-        self.parameter_learning.fit(net, dataset, node, parent_set)
+        match self.cache_persistent.get(&parent_set) {
+            // TODO: Bettern not clone `params`, useless clock cycles, RAM use and I/O
+            // not cloning requires a minor and reasoned refactoring across the library
+            Some(params) => params.clone(),
+            None => {
+                let params = self.parameter_learning.fit(net, dataset, node, parent_set.clone());
+                self.cache_persistent.insert(parent_set, params.clone());
+                params
+            }
+        }
     }
 }

reCTBN/tests/structure_learning.rs

@@ -473,9 +473,11 @@ pub fn chi_square_call() {
     let chi_sq = ChiSquare::new(1e-4);
 
     assert!(chi_sq.call(&net, N1, N3, &separation_set, &data, &mut cache));
+    let mut cache = Cache::new(&parameter_learning);
     assert!(!chi_sq.call(&net, N3, N1, &separation_set, &data, &mut cache));
     assert!(!chi_sq.call(&net, N3, N2, &separation_set, &data, &mut cache));
     separation_set.insert(N1);
+    let mut cache = Cache::new(&parameter_learning);
     assert!(chi_sq.call(&net, N2, N3, &separation_set, &data, &mut cache));
 }
 
@@ -493,9 +495,11 @@ pub fn f_call() {
 
 
     assert!(f.call(&net, N1, N3, &separation_set, &data, &mut cache));
+    let mut cache = Cache::new(&parameter_learning);
     assert!(!f.call(&net, N3, N1, &separation_set, &data, &mut cache));
     assert!(!f.call(&net, N3, N2, &separation_set, &data, &mut cache));
     separation_set.insert(N1);
+    let mut cache = Cache::new(&parameter_learning);
     assert!(f.call(&net, N2, N3, &separation_set, &data, &mut cache));
 }