reCTBN/src/parameter_learning.rs

@@ -1,7 +1,6 @@
 //! Module containing methods used to learn the parameters.
 
-use std::collections::{BTreeSet, HashMap};
-use std::mem;
+use std::collections::BTreeSet;
 
 use ndarray::prelude::*;
 
@@ -165,69 +164,3 @@ impl ParameterLearning for BayesianApproach {
         return n;
     }
 }
-
-// TODO: Move to constraint_based_algorithm.rs
-pub struct Cache<'a, P: ParameterLearning> {
-    parameter_learning: &'a P,
-    cache_persistent_small: HashMap<Option<BTreeSet<usize>>, Params>,
-    cache_persistent_big: HashMap<Option<BTreeSet<usize>>, Params>,
-    parent_set_size_small: usize,
-}
-
-impl<'a, P: ParameterLearning> Cache<'a, P> {
-    pub fn new(parameter_learning: &'a P) -> Cache<'a, P> {
-        Cache {
-            parameter_learning,
-            cache_persistent_small: HashMap::new(),
-            cache_persistent_big: HashMap::new(),
-            parent_set_size_small: 0,
-        }
-    }
-    pub fn fit<T: process::NetworkProcess>(
-        &mut self,
-        net: &T,
-        dataset: &Dataset,
-        node: usize,
-        parent_set: Option<BTreeSet<usize>>,
-    ) -> Params {
-        let parent_set_len = parent_set.as_ref().unwrap().len();
-        if parent_set_len > self.parent_set_size_small + 1 {
-            //self.cache_persistent_small = self.cache_persistent_big;
-            mem::swap(
-                &mut self.cache_persistent_small,
-                &mut self.cache_persistent_big,
-            );
-            self.cache_persistent_big = HashMap::new();
-            self.parent_set_size_small += 1;
-        }
-
-        if parent_set_len > self.parent_set_size_small {
-            match self.cache_persistent_big.get(&parent_set) {
-                // TODO: Better 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_big.insert(parent_set, params.clone());
-                    params
-                }
-            }
-        } else {
-            match self.cache_persistent_small.get(&parent_set) {
-                // TODO: Better 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_small
-                        .insert(parent_set, params.clone());
-                    params
-                }
-            }
-        }
-    }
-}

reCTBN/src/structure_learning/constraint_based_algorithm.rs

@@ -1,17 +1,84 @@
 //! Module containing constraint based algorithms like CTPC and Hiton.
 
+use crate::params::Params;
 use itertools::Itertools;
 use rayon::iter::{IntoParallelIterator, ParallelIterator};
 use rayon::prelude::ParallelExtend;
-use std::collections::BTreeSet;
+use std::collections::{BTreeSet, HashMap};
+use std::mem;
 use std::usize;
 
 use super::hypothesis_test::*;
-use crate::parameter_learning::{Cache, ParameterLearning};
+use crate::parameter_learning::ParameterLearning;
 use crate::process;
 use crate::structure_learning::StructureLearningAlgorithm;
 use crate::tools::Dataset;
 
+pub struct Cache<'a, P: ParameterLearning> {
+    parameter_learning: &'a P,
+    cache_persistent_small: HashMap<Option<BTreeSet<usize>>, Params>,
+    cache_persistent_big: HashMap<Option<BTreeSet<usize>>, Params>,
+    parent_set_size_small: usize,
+}
+
+impl<'a, P: ParameterLearning> Cache<'a, P> {
+    pub fn new(parameter_learning: &'a P) -> Cache<'a, P> {
+        Cache {
+            parameter_learning,
+            cache_persistent_small: HashMap::new(),
+            cache_persistent_big: HashMap::new(),
+            parent_set_size_small: 0,
+        }
+    }
+    pub fn fit<T: process::NetworkProcess>(
+        &mut self,
+        net: &T,
+        dataset: &Dataset,
+        node: usize,
+        parent_set: Option<BTreeSet<usize>>,
+    ) -> Params {
+        let parent_set_len = parent_set.as_ref().unwrap().len();
+        if parent_set_len > self.parent_set_size_small + 1 {
+            //self.cache_persistent_small = self.cache_persistent_big;
+            mem::swap(
+                &mut self.cache_persistent_small,
+                &mut self.cache_persistent_big,
+            );
+            self.cache_persistent_big = HashMap::new();
+            self.parent_set_size_small += 1;
+        }
+
+        if parent_set_len > self.parent_set_size_small {
+            match self.cache_persistent_big.get(&parent_set) {
+                // TODO: Better 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_big.insert(parent_set, params.clone());
+                    params
+                }
+            }
+        } else {
+            match self.cache_persistent_small.get(&parent_set) {
+                // TODO: Better 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_small
+                        .insert(parent_set, params.clone());
+                    params
+                }
+            }
+        }
+    }
+}
+
 pub struct CTPC<P: ParameterLearning> {
     parameter_learning: P,
     Ftest: F,

reCTBN/src/structure_learning/hypothesis_test.rs

@@ -6,6 +6,7 @@ use ndarray::{Array3, Axis};
 use statrs::distribution::{ChiSquared, ContinuousCDF, FisherSnedecor};
 
 use crate::params::*;
+use crate::structure_learning::constraint_based_algorithm::Cache;
 use crate::{parameter_learning, process, tools::Dataset};
 
 pub trait HypothesisTest {
@@ -16,7 +17,7 @@ pub trait HypothesisTest {
         parent_node: usize,
         separation_set: &BTreeSet<usize>,
         dataset: &Dataset,
-        cache: &mut parameter_learning::Cache<P>,
+        cache: &mut Cache<P>,
     ) -> bool
     where
         T: process::NetworkProcess,
@@ -86,7 +87,7 @@ impl HypothesisTest for F {
         parent_node: usize,
         separation_set: &BTreeSet<usize>,
         dataset: &Dataset,
-        cache: &mut parameter_learning::Cache<P>,
+        cache: &mut Cache<P>,
     ) -> bool
     where
         T: process::NetworkProcess,
@@ -226,7 +227,7 @@ impl HypothesisTest for ChiSquare {
         parent_node: usize,
         separation_set: &BTreeSet<usize>,
         dataset: &Dataset,
-        cache: &mut parameter_learning::Cache<P>,
+        cache: &mut Cache<P>,
     ) -> bool
     where
         T: process::NetworkProcess,

reCTBN/tests/structure_learning.rs

@@ -7,7 +7,6 @@ use ndarray::{arr1, arr2, arr3};
 use reCTBN::process::ctbn::*;
 use reCTBN::process::NetworkProcess;
 use reCTBN::parameter_learning::BayesianApproach;
-use reCTBN::parameter_learning::Cache;
 use reCTBN::params;
 use reCTBN::structure_learning::hypothesis_test::*;
 use reCTBN::structure_learning::constraint_based_algorithm::*;