Changed the return type for ParameterLearning::fit from tuple to Param

src/parameter_learning.rs

@@ -12,7 +12,7 @@ pub trait ParameterLearning{
         dataset: &tools::Dataset,
         node: usize,
         parent_set: Option<BTreeSet<usize>>,
-    ) -> (Array3<f64>, Array3<usize>, Array2<f64>);
+    ) -> Params;
 }
 
 pub fn sufficient_statistics<T:network::Network>(
@@ -84,8 +84,7 @@ impl ParameterLearning for MLE {
         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
+    ) -> Params {
 
         //Use parent_set from parameter if present. Otherwise use parent_set from network.
         let parent_set = match parent_set {
@@ -107,7 +106,21 @@ impl ParameterLearning for MLE {
             .for_each(|(mut C, diag)| {
                 C.diag_mut().assign(&diag);
             });
-        return (CIM, M, T);
+        
+
+
+        let mut n: Params = net.get_node(node).clone();
+
+        match n {
+            Params::DiscreteStatesContinousTime(ref mut dsct) => {
+                dsct.set_cim_unchecked(CIM);
+                dsct.set_transitions(M);
+                dsct.set_residence_time(T);
+
+
+            }
+        };
+        return n;
     }
 }
 
@@ -123,7 +136,7 @@ impl ParameterLearning for BayesianApproach {
         dataset: &tools::Dataset,
         node: usize,
         parent_set: Option<BTreeSet<usize>>,
-    ) -> (Array3<f64>, Array3<usize>, Array2<f64>) {
+    ) -> Params {
         //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.
@@ -150,7 +163,21 @@ impl ParameterLearning for BayesianApproach {
             .for_each(|(mut C, diag)| {
                 C.diag_mut().assign(&diag);
             });
-        return (CIM, M, T);
+
+
+
+        let mut n: Params = net.get_node(node).clone();
+
+        match n {
+            Params::DiscreteStatesContinousTime(ref mut dsct) => {
+                dsct.set_cim_unchecked(CIM);
+                dsct.set_transitions(M);
+                dsct.set_residence_time(T);
+
+
+            }
+        };
+        return n;
     }
 }
 
@@ -166,7 +193,7 @@ impl<P: ParameterLearning> Cache<P> {
         net: &T,
         node: usize,
         parent_set: Option<BTreeSet<usize>>,
-    ) -> (Array3<f64>, Array3<usize>, Array2<f64>) {
+    ) -> Params {
         self.parameter_learning.fit(net, &self.dataset, node, parent_set)
     }
 }

src/params.rs

@@ -55,7 +55,8 @@ pub trait ParamsTrait {
 }
 
 /// The Params enum is the core element for building different types of nodes. The goal is to
-/// define all the supported type of parameters.
+/// define all the supported type of Parameters
+#[derive(Clone)]
 #[enum_dispatch]
 pub enum Params {
     DiscreteStatesContinousTime(DiscreteStatesContinousTimeParams),
@@ -72,11 +73,12 @@ pub enum Params {
 ///     realization of the parent set
 ///     - **residence_time**: permanence time in each possible states given a specific
 ///     realization of the parent set
+#[derive(Clone)]
 pub struct DiscreteStatesContinousTimeParams {
     label: String,
     domain: BTreeSet<String>,
     cim: Option<Array3<f64>>,
-    transitions: Option<Array3<u64>>,
+    transitions: Option<Array3<usize>>,
     residence_time: Option<Array2<f64>>,
 }
 
@@ -112,14 +114,19 @@ impl DiscreteStatesContinousTimeParams {
     }
 
 
+    ///Unchecked version of the setter function for CIM.
+    pub fn set_cim_unchecked(&mut self, cim: Array3<f64>) {
+        self.cim = Some(cim);
+    }
+
     ///Getter function for transitions
-    pub fn get_transitions(&self) -> &Option<Array3<u64>> {
+    pub fn get_transitions(&self) -> &Option<Array3<usize>> {
         &self.transitions
     }
 
 
     ///Setter function for transitions
-    pub fn set_transitions(&mut self, transitions: Array3<u64>) {
+    pub fn set_transitions(&mut self, transitions: Array3<usize>) {
         self.transitions = Some(transitions);
     }
 

src/structure_learning/hypothesis_test.rs

@@ -5,46 +5,39 @@ use statrs::distribution::{ChiSquared, ContinuousCDF};
 
 use crate::network;
 use crate::parameter_learning;
-use crate::params::ParamsTrait;
+use crate::params::*;
 use std::collections::BTreeSet;
 
 pub trait HypothesisTest {
-
     fn call<T, P>(
         &self,
         net: &T,
         child_node: usize,
         parent_node: usize,
         separation_set: &BTreeSet<usize>,
-        cache: &mut parameter_learning::Cache<P>
+        cache: &mut parameter_learning::Cache<P>,
     ) -> bool
     where
         T: network::Network,
         P: parameter_learning::ParameterLearning;
-
 }
 
-
 pub struct ChiSquare {
     alpha: f64,
 }
 
-pub struct F {
-
-}
+pub struct F {}
 
 impl ChiSquare {
-    pub fn new( alpha: f64) -> ChiSquare {
-        ChiSquare {
-            alpha
-        }
+    pub fn new(alpha: f64) -> ChiSquare {
+        ChiSquare { alpha }
     }
     pub fn compare_matrices(
         &self,
         i: usize,
         M1: &Array3<usize>,
         j: usize,
-        M2: &Array3<usize>
+        M2: &Array3<usize>,
     ) -> bool {
         // Bregoli, A., Scutari, M. and Stella, F., 2021.
         // A constraint-based algorithm for the structural learning of
@@ -87,7 +80,7 @@ impl ChiSquare {
         //        =====         2    1
         //     x'ϵVal /X \
         //            \ i/
-        let mut X_2 = ( &K * &M2 - &L * &M1 ).mapv(|a| a.powi(2)) / (&M2 + &M1);
+        let mut X_2 = (&K * &M2 - &L * &M1).mapv(|a| a.powi(2)) / (&M2 + &M1);
         println!("M1: {:?}", M1);
         println!("M2: {:?}", M2);
         println!("L*M1: {:?}", (L * &M1));
@@ -109,24 +102,38 @@ impl HypothesisTest for ChiSquare {
         child_node: usize,
         parent_node: usize,
         separation_set: &BTreeSet<usize>,
-        cache: &mut parameter_learning::Cache<P>
+        cache: &mut parameter_learning::Cache<P>,
     ) -> bool
     where
         T: network::Network,
-        P: parameter_learning::ParameterLearning {
+        P: parameter_learning::ParameterLearning,
+    {
         // Prendo dalla cache l'apprendimento dei parametri, che sarebbe una CIM
         // di dimensione nxn
         //  (CIM, M, T)
-        let ( _, M_small, _) = cache.fit(net, child_node, Some(separation_set.clone()));
-        // 
+        let P_small = match cache.fit(net, child_node, Some(separation_set.clone())){
+            Params::DiscreteStatesContinousTime(node) => node
+        };
+        //
         let mut extended_separation_set = separation_set.clone();
         extended_separation_set.insert(parent_node);
-        let ( _, M_big, _) = cache.fit(net, child_node, Some(extended_separation_set.clone()));
+
+        let P_big = match cache.fit(net, child_node, Some(extended_separation_set.clone())){
+            Params::DiscreteStatesContinousTime(node) => node
+        };
         // Commentare qui
-        let partial_cardinality_product:usize = extended_separation_set.iter().take_while(|x| **x != parent_node).map(|x| net.get_node(*x).get_reserved_space_as_parent()).product();
-        for idx_M_big in 0..M_big.shape()[0] {
-            let idx_M_small: usize = idx_M_big%partial_cardinality_product + (idx_M_big/(partial_cardinality_product*net.get_node(parent_node).get_reserved_space_as_parent()))*partial_cardinality_product;
-            if ! self.compare_matrices(idx_M_small, &M_small, idx_M_big, &M_big) {
+        let partial_cardinality_product: usize = extended_separation_set
+            .iter()
+            .take_while(|x| **x != parent_node)
+            .map(|x| net.get_node(*x).get_reserved_space_as_parent())
+            .product();
+        for idx_M_big in 0..P_big.get_transitions().as_ref().unwrap().shape()[0] {
+            let idx_M_small: usize = idx_M_big % partial_cardinality_product
+                + (idx_M_big
+                    / (partial_cardinality_product
+                        * net.get_node(parent_node).get_reserved_space_as_parent()))
+                    * partial_cardinality_product;
+            if !self.compare_matrices(idx_M_small, P_small.get_transitions().as_ref().unwrap(), idx_M_big, P_big.get_transitions().as_ref().unwrap()) {
                 return false;
             }
         }

tests/parameter_learning.rs

@@ -40,10 +40,11 @@ fn learn_binary_cim<T: ParameterLearning>(pl: T) {
     }
 
     let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259));
-    let (CIM, M, T) = pl.fit(&net, &data, 1, None);
-    print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
-    assert_eq!(CIM.shape(), [2, 2, 2]);
-    assert!(CIM.abs_diff_eq(
+    let p = match pl.fit(&net, &data, 1, None) {
+        params::Params::DiscreteStatesContinousTime(p) => p
+    };
+    assert_eq!(p.get_cim().as_ref().unwrap().shape(), [2, 2, 2]);
+    assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
         &arr3(&[[[-1.0, 1.0], [4.0, -4.0]], [[-6.0, 6.0], [2.0, -2.0]],]),
         0.1
     ));
@@ -98,10 +99,11 @@ fn learn_ternary_cim<T: ParameterLearning>(pl: T) {
     }
 
     let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
-    let (CIM, M, T) = pl.fit(&net, &data, 1, None);
-    print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
-    assert_eq!(CIM.shape(), [3, 3, 3]);
-    assert!(CIM.abs_diff_eq(
+    let p = match pl.fit(&net, &data, 1, None){
+        params::Params::DiscreteStatesContinousTime(p) => p
+    };
+    assert_eq!(p.get_cim().as_ref().unwrap().shape(), [3, 3, 3]);
+    assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
         &arr3(&[
             [[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]],
             [[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]],
@@ -160,10 +162,11 @@ fn learn_ternary_cim_no_parents<T: ParameterLearning>(pl: T) {
     }
 
     let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
-    let (CIM, M, T) = pl.fit(&net, &data, 0, None);
-    print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
-    assert_eq!(CIM.shape(), [1, 3, 3]);
-    assert!(CIM.abs_diff_eq(
+    let p = match pl.fit(&net, &data, 0, None){
+        params::Params::DiscreteStatesContinousTime(p) => p
+    };
+    assert_eq!(p.get_cim().as_ref().unwrap().shape(), [1, 3, 3]);
+    assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
         &arr3(&[[[-3.0, 2.0, 1.0], [1.5, -2.0, 0.5], [0.4, 0.6, -1.0]]]),
         0.1
     ));
@@ -288,10 +291,11 @@ fn learn_mixed_discrete_cim<T: ParameterLearning>(pl: T) {
     }
 
     let data = trajectory_generator(&net, 300, 300.0, Some(6347747169756259));
-    let (CIM, M, T) = pl.fit(&net, &data, 2, None);
-    print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
-    assert_eq!(CIM.shape(), [9, 4, 4]);
-    assert!(CIM.abs_diff_eq(
+    let p = match pl.fit(&net, &data, 2, None){
+        params::Params::DiscreteStatesContinousTime(p) => p
+    };
+    assert_eq!(p.get_cim().as_ref().unwrap().shape(), [9, 4, 4]);
+    assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
         &arr3(&[
             [
                 [-1.0, 0.5, 0.3, 0.2],