diff --git a/deps/reCTBN b/deps/reCTBN
index e638a62..0eb427e 160000
--- a/deps/reCTBN
+++ b/deps/reCTBN
@@ -1 +1 @@
-Subproject commit e638a627bb1efb675d4242eff0bb543715b55ddc
+Subproject commit 0eb427e5cfda8b06cc4a1dc24725316b01237f75
diff --git a/src/main.rs b/src/main.rs
index d59d2d4..fee9bd8 100644
--- a/src/main.rs
+++ b/src/main.rs
@@ -4,6 +4,7 @@ use csv;
 use json;
 use std::collections::BTreeSet;
 use std::fs;
+use std::process::exit;
 use std::time::Instant;
 
 use reCTBN::parameter_learning::MLE;
@@ -85,7 +86,7 @@ where
     Ok(())
 }
 
-fn structure_learning_CTPC(net: CtbnNetwork, dataset: &Dataset) {
+fn structure_learning_CTPC(net: CtbnNetwork, dataset: &Dataset) -> CtbnNetwork {
     // Initialize the hypothesis tests to pass to the CTPC with their
     // respective significance level `alpha`
     let f = F::new(1e-6);
@@ -95,17 +96,17 @@ fn structure_learning_CTPC(net: CtbnNetwork, dataset: &Dataset) {
     //Initialize CTPC
     let ctpc = CTPC::new(parameter_learning, f, chi_sq);
     // Learn the structure of the network from the generated trajectory
-    ctpc.fit_transform(net, dataset);
+    ctpc.fit_transform(net, dataset)
 }
 
 fn main() {
     let file_path = "./networks-settings.json";
-    let csv_path = "./results.csv";
     eprintln!("Opening file {}...", file_path);
     let file_content =
         fs::read_to_string(file_path).expect("File not found! Check the README for instructions!");
     let parsed_json = json::parse(&file_content).unwrap();
 
+    let csv_path = "./results.csv";
     write_csv_record(
         csv_path,
         &[
@@ -113,6 +114,7 @@ fn main() {
             "domain_cardinality",
             "density",
             "duration",
+            "f1_score",
         ],
     )
     .unwrap();
@@ -136,13 +138,29 @@ fn main() {
             parsed_json[idx]["cg_seed"].as_u64(),
             parsed_json[idx]["tg_seed"].as_u64(),
         );
+        let strucure_original = net.get_adj_matrix().unwrap().clone();
+        let relevant_elements = strucure_original.iter().filter(|&elem| *elem == 1).count() as f64;
+        if relevant_elements == 0 as f64 {
+            eprintln!(
+                "[{}/{}] The network gnerated is empty! Change seeds for this network!",
+                idx + 1,
+                benchmarks_cardinality
+            );
+            exit(1)
+        }
+        eprintln!(
+            "[{}/{}] Original strucure:\n{:?}",
+            idx + 1,
+            benchmarks_cardinality,
+            strucure_original
+        );
         eprintln!(
             "[{}/{}] Structure learning CTPC...",
             idx + 1,
             benchmarks_cardinality
         );
         let start = Instant::now();
-        structure_learning_CTPC(net, &dataset);
+        let net = structure_learning_CTPC(net, &dataset);
         let duration = start.elapsed();
         eprintln!(
             "[{}/{}] Strucure learned with CTPC in {:?}.",
@@ -150,6 +168,35 @@ fn main() {
             benchmarks_cardinality,
             duration
         );
+        let structure_generated = net.get_adj_matrix().unwrap();
+        let retrieved_elements = structure_generated
+            .iter()
+            .filter(|&elem| *elem == 1)
+            .count() as f64;
+        eprintln!(
+            "[{}/{}] Discovered strucure:\n{}",
+            idx + 1,
+            benchmarks_cardinality,
+            structure_generated
+        );
+        let sum_structure = strucure_original + structure_generated;
+        eprintln!(
+            "[{}/{}] strucure_original + structure_generated value:\n{}",
+            idx + 1,
+            benchmarks_cardinality,
+            sum_structure
+        );
+        let true_positives = sum_structure.iter().filter(|&elem| *elem == 2).count() as f64;
+        // https://en.wikipedia.org/wiki/F-score
+        let precision = true_positives / retrieved_elements;
+        let recall = true_positives / relevant_elements;
+        let f1_score = 2.0 * (precision * recall) / (precision + recall);
+        eprintln!(
+            "[{}/{}] f1_score: {}",
+            idx + 1,
+            benchmarks_cardinality,
+            f1_score
+        );
         write_csv_record(
             csv_path,
             &[
@@ -163,6 +210,7 @@ fn main() {
                     .to_string(),
                 parsed_json[idx]["density"].as_f64().unwrap().to_string(),
                 duration.as_millis().to_string(),
+                f1_score.to_string(),
             ],
         )
         .unwrap();