Expanded Hypothesis test

src/parameter_learning.rs

@@ -157,16 +157,16 @@ impl ParameterLearning for BayesianApproach {
 
 pub struct Cache<P: ParameterLearning> {
     parameter_learning: P,
+    dataset: tools::Dataset,
 }
 
 impl<P: ParameterLearning> Cache<P> {
     pub fn fit<T:network::Network>(
         &mut self,
         net: &T,
-        dataset: &tools::Dataset,
         node: usize,
         parent_set: Option<BTreeSet<usize>>,
     ) -> (Array3<f64>, Array3<usize>, Array2<f64>) {
-        self.parameter_learning.fit(net, dataset, node, parent_set)
+        self.parameter_learning.fit(net, &self.dataset, node, parent_set)
     }
 }

src/structure_learning/hypothesis_test.rs

@@ -5,6 +5,7 @@ use statrs::distribution::{ChiSquared, ContinuousCDF};
 
 use crate::network;
 use crate::parameter_learning;
+use crate::params::ParamsTrait;
 use std::collections::BTreeSet;
 
 pub trait HypothesisTest {
@@ -15,7 +16,7 @@ pub trait HypothesisTest {
         child_node: usize,
         parent_node: usize,
         separation_set: &BTreeSet<usize>,
-        cache: parameter_learning::Cache<P>
+        cache: &mut parameter_learning::Cache<P>
     ) -> bool
     where
         T: network::Network,
@@ -39,7 +40,8 @@ impl ChiSquare {
         }
     }
     pub fn compare_matrices(
-        &self, i: usize,
+        &self,
+        i: usize,
         M1: &Array3<usize>,
         j: usize,
         M2: &Array3<usize>
@@ -107,11 +109,27 @@ impl HypothesisTest for ChiSquare {
         child_node: usize,
         parent_node: usize,
         separation_set: &BTreeSet<usize>,
-        cache: parameter_learning::Cache<P>
+        cache: &mut parameter_learning::Cache<P>
     ) -> bool
     where
         T: network::Network,
         P: parameter_learning::ParameterLearning {
-        todo!()
+        // 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 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()));
+        // 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) {
+                return false;
+            }
+        }
+        return true;
     }
 }