diff --git a/docs/BoostAODE.docx b/docs/BoostAODE.docx
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index 0000000..ec04a70
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diff --git a/lib/argparse b/lib/argparse
new file mode 160000
index 0000000..69dabd8
--- /dev/null
+++ b/lib/argparse
@@ -0,0 +1 @@
+Subproject commit 69dabd88a8e6680b1a1a18397eb3e165e4019ce6
diff --git a/lib/catch2 b/lib/catch2
index 863c662..766541d 160000
--- a/lib/catch2
+++ b/lib/catch2
@@ -1 +1 @@
-Subproject commit 863c662c0eff026300f4d729a7054e90d6d12cdd
+Subproject commit 766541d12d64845f5232a1ce4e34a85e83506b09
diff --git a/lib/libxlsxwriter b/lib/libxlsxwriter
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--- /dev/null
+++ b/lib/libxlsxwriter
@@ -0,0 +1 @@
+Subproject commit 29355a0887475488c7cc470ad43cc867fcfa92e2
diff --git a/src/BayesNet/BoostAODE.cc b/src/BayesNet/BoostAODE.cc
index efb95e6..3b1d0f1 100644
--- a/src/BayesNet/BoostAODE.cc
+++ b/src/BayesNet/BoostAODE.cc
@@ -121,6 +121,8 @@ namespace bayesnet {
     }
     void BoostAODE::trainModel(const torch::Tensor& weights)
     {
+        // Algorithm based on the adaboost algorithm for classification
+        // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
         std::unordered_set<int> featuresUsed;
         if (selectFeatures) {
             featuresUsed = initializeModels();
@@ -132,9 +134,8 @@ namespace bayesnet {
         // Variables to control the accuracy finish condition
         double priorAccuracy = 0.0;
         double delta = 1.0;
-        double threshold = 1e-4;
-        int count = 0; // number of times the accuracy is lower than the threshold
-        fitted = true; // to enable predict
+        double convergence_threshold = 1e-4;
+        int count = 0; // number of times the accuracy is lower than the convergence_threshold
         // Step 0: Set the finish condition
         // if not repeatSparent a finish condition is run out of features
         // n_models == maxModels
@@ -191,10 +192,12 @@ namespace bayesnet {
                 } else {
                     delta = accuracy - priorAccuracy;
                 }
-                if (delta < threshold) {
+                if (delta < convergence_threshold) {
                     count++;
                 }
+                priorAccuracy = accuracy;
             }
+            // epsilon_t > 0.5 => inverse the weights policy (plot ln(wt))
             exitCondition = n_models >= maxModels && repeatSparent || epsilon_t > 0.5 || count > tolerance;
         }
         if (featuresUsed.size() != features.size()) {
@@ -202,6 +205,7 @@ namespace bayesnet {
             status = WARNING;
         }
         notes.push_back("Number of models: " + std::to_string(n_models));
+        fitted = true;
     }
     std::vector<std::string> BoostAODE::graph(const std::string& title) const
     {