diff --git a/reCTBN/src/parameter_learning.rs b/reCTBN/src/parameter_learning.rs
index 10f0257..bdb5d4a 100644
--- a/reCTBN/src/parameter_learning.rs
+++ b/reCTBN/src/parameter_learning.rs
@@ -169,6 +169,12 @@ pub struct Cache<P: ParameterLearning> {
 }
 
 impl<P: ParameterLearning> Cache<P> {
+    pub fn new(parameter_learning: P, dataset: tools::Dataset) -> Cache<P> {
+        Cache {
+            parameter_learning,
+            dataset,
+        }
+    }
     pub fn fit<T: network::Network>(
         &mut self,
         net: &T,
diff --git a/reCTBN/src/structure_learning/hypothesis_test.rs b/reCTBN/src/structure_learning/hypothesis_test.rs
index 5ddcc51..4f2ce18 100644
--- a/reCTBN/src/structure_learning/hypothesis_test.rs
+++ b/reCTBN/src/structure_learning/hypothesis_test.rs
@@ -30,6 +30,8 @@ impl ChiSquare {
     pub fn new(alpha: f64) -> ChiSquare {
         ChiSquare { alpha }
     }
+    // Restituisce true quando le matrici sono molto simili, quindi indipendenti
+    // false quando sono diverse, quindi dipendenti
     pub fn compare_matrices(
         &self,
         i: usize,
@@ -69,6 +71,7 @@ impl ChiSquare {
             let n = K.len();
             K.into_shape((n, 1)).unwrap()
         };
+        println!("K: {:?}", K);
         let L = 1.0 / &K;
         //        =====                   2
         //         \      (K . M  - L . M)
@@ -89,10 +92,13 @@ impl ChiSquare {
         let n = ChiSquared::new((X_2.dim() - 1) as f64).unwrap();
         println!("CHI^2: {:?}", n);
         println!("CHI^2 CDF: {:?}", X_2.mapv(|x| n.cdf(x)));
-        X_2.into_iter().all(|x| n.cdf(x) < (1.0 - self.alpha))
+        let ret = X_2.into_iter().all(|x| n.cdf(x) < (1.0 - self.alpha));
+        println!("test: {:?}", ret);
+        ret
     }
 }
 
+// ritorna false quando sono dipendenti e false quando sono indipendenti
 impl HypothesisTest for ChiSquare {
     fn call<T, P>(
         &self,
diff --git a/reCTBN/tests/structure_learning.rs b/reCTBN/tests/structure_learning.rs
index 81a4ed3..a1667c2 100644
--- a/reCTBN/tests/structure_learning.rs
+++ b/reCTBN/tests/structure_learning.rs
@@ -6,6 +6,8 @@ use std::collections::BTreeSet;
 use ndarray::{arr1, arr2, arr3};
 use reCTBN::ctbn::*;
 use reCTBN::network::Network;
+use reCTBN::parameter_learning::BayesianApproach;
+use reCTBN::parameter_learning::Cache;
 use reCTBN::params;
 use reCTBN::structure_learning::hypothesis_test::*;
 use reCTBN::structure_learning::score_based_algorithm::*;
@@ -455,3 +457,22 @@ pub fn chi_square_compare_matrices_3() {
     let chi_sq = ChiSquare::new(0.1);
     assert!(chi_sq.compare_matrices(i, &M1, j, &M2));
 }
+
+
+#[test]
+pub fn chi_square_call() {
+
+    let (net, data) = get_mixed_discrete_net_3_nodes_with_data();
+    let N3: usize = 2;
+    let N2: usize = 1;
+    let N1: usize = 0;
+    let separation_set = BTreeSet::new();
+    let parameter_learning = BayesianApproach { alpha: 1, tau:1.0 };
+    let mut cache = Cache::new(parameter_learning, data);
+    let chi_sq = ChiSquare::new(0.0001);
+
+    assert!(chi_sq.call(&net, N1, N3, &separation_set, &mut cache));
+    assert!(!chi_sq.call(&net, N3, N1, &separation_set, &mut cache));
+    assert!(!chi_sq.call(&net, N3, N2, &separation_set, &mut cache));
+    assert!(chi_sq.call(&net, N2, N3, &separation_set, &mut cache));
+}