diff --git a/src/BayesNet/KDBNew.cc b/src/BayesNet/KDBNew.cc
index 8adf3b4..f2f6e46 100644
--- a/src/BayesNet/KDBNew.cc
+++ b/src/BayesNet/KDBNew.cc
@@ -10,15 +10,12 @@ namespace bayesnet {
         className = className_;
         Xf = X_;
         y = y_;
-        model.initialize();
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
         fit_local_discretization(states, y);
         generateTensorXFromVector();
         // We have discretized the input data
-        // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
-        cout << "KDBNew: Fitting model" << endl;
+        // 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network
         KDB::fit(KDB::Xv, KDB::yv, features, className, states);
-        cout << "KDBNew: Model fitted" << endl;
         localDiscretizationProposal(states, model);
         generateTensorXFromVector();
         Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
@@ -26,20 +23,10 @@ namespace bayesnet {
         model.fit(KDB::Xv, KDB::yv, features, className);
         return *this;
     }
-    void KDBNew::train()
-    {
-        KDB::train();
-    }
     Tensor KDBNew::predict(Tensor& X)
     {
-        auto Xtd = torch::zeros_like(X, torch::kInt32);
-        for (int i = 0; i < X.size(0); ++i) {
-            auto Xt = vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));
-            auto Xd = discretizers[features[i]]->transform(Xt);
-            Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));
-        }
-        cout << "KDBNew Xtd: " << Xtd.sizes() << endl;
-        return KDB::predict(Xtd);
+        auto Xt = prepareX(X);
+        return KDB::predict(Xt);
     }
     vector<string> KDBNew::graph(const string& name)
     {
diff --git a/src/BayesNet/KDBNew.h b/src/BayesNet/KDBNew.h
index 2bcd213..63ae9e9 100644
--- a/src/BayesNet/KDBNew.h
+++ b/src/BayesNet/KDBNew.h
@@ -13,7 +13,6 @@ namespace bayesnet {
         KDBNew& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
         vector<string> graph(const string& name = "KDB") override;
         Tensor predict(Tensor& X) override;
-        void train() override;
         static inline string version() { return "0.0.1"; };
     };
 }
diff --git a/src/BayesNet/Proposal.cc b/src/BayesNet/Proposal.cc
index 2156b9a..c1d3626 100644
--- a/src/BayesNet/Proposal.cc
+++ b/src/BayesNet/Proposal.cc
@@ -47,25 +47,25 @@ namespace bayesnet {
             //
             //
             //
-            auto tmp = discretizers[feature]->transform(xvf);
-            Xv[index] = tmp;
-            auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
-            iota(xStates.begin(), xStates.end(), 0);
-            //Update new states of the feature/node
-            states[feature] = xStates;
+            // auto tmp = discretizers[feature]->transform(xvf);
+            // Xv[index] = tmp;
+            // auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
+            // iota(xStates.begin(), xStates.end(), 0);
+            // //Update new states of the feature/node
+            // states[feature] = xStates;
+        }
+        if (upgrade) {
+            // Discretize again X (only the affected indices) with the new fitted discretizers
+            for (auto index : indicesToReDiscretize) {
+                auto Xt_ptr = Xf.index({ index }).data_ptr<float>();
+                auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
+                Xv[index] = discretizers[pFeatures[index]]->transform(Xt);
+                auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
+                iota(xStates.begin(), xStates.end(), 0);
+                //Update new states of the feature/node
+                states[pFeatures[index]] = xStates;
+            }
         }
-        // if (upgrade) {
-        //     // Discretize again X (only the affected indices) with the new fitted discretizers
-        //     for (auto index : indicesToReDiscretize) {
-        //         auto Xt_ptr = Xf.index({ index }).data_ptr<float>();
-        //         auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
-        //         Xv[index] = discretizers[pFeatures[index]]->transform(Xt);
-        //         auto xStates = vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
-        //         iota(xStates.begin(), xStates.end(), 0);
-        //         //Update new states of the feature/node
-        //         states[pFeatures[index]] = xStates;
-        //     }
-        // }
     }
     void Proposal::fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y)
     {
@@ -89,4 +89,14 @@ namespace bayesnet {
         iota(yStates.begin(), yStates.end(), 0);
         states[pClassName] = yStates;
     }
+    torch::Tensor Proposal::prepareX(torch::Tensor& X)
+    {
+        auto Xtd = torch::zeros_like(X, torch::kInt32);
+        for (int i = 0; i < X.size(0); ++i) {
+            auto Xt = vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));
+            auto Xd = discretizers[pFeatures[i]]->transform(Xt);
+            Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));
+        }
+        return Xtd;
+    }
 }
\ No newline at end of file
diff --git a/src/BayesNet/Proposal.h b/src/BayesNet/Proposal.h
index 981c22c..a5650b4 100644
--- a/src/BayesNet/Proposal.h
+++ b/src/BayesNet/Proposal.h
@@ -5,6 +5,7 @@
 #include <torch/torch.h>
 #include "Network.h"
 #include "CPPFImdlp.h"
+#include "Classifier.h"
 
 namespace bayesnet {
     class Proposal {
@@ -12,6 +13,7 @@ namespace bayesnet {
         Proposal(vector<vector<int>>& Xv_, vector<int>& yv_, vector<string>& features_, string& className_);
         virtual ~Proposal();
     protected:
+        torch::Tensor prepareX(torch::Tensor& X);
         void localDiscretizationProposal(map<string, vector<int>>& states, Network& model);
         void fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y);
         torch::Tensor Xf; // X continuous nxm tensor
diff --git a/src/BayesNet/TANNew.cc b/src/BayesNet/TANNew.cc
index e0e1f0b..15a1eaf 100644
--- a/src/BayesNet/TANNew.cc
+++ b/src/BayesNet/TANNew.cc
@@ -15,9 +15,7 @@ namespace bayesnet {
         generateTensorXFromVector();
         // We have discretized the input data
         // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
-        cout << "TANNew: Fitting model" << endl;
         TAN::fit(TAN::Xv, TAN::yv, features, className, states);
-        cout << "TANNew: Model fitted" << endl;
         localDiscretizationProposal(states, model);
         generateTensorXFromVector();
         Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
@@ -27,14 +25,8 @@ namespace bayesnet {
     }
     Tensor TANNew::predict(Tensor& X)
     {
-        auto Xtd = torch::zeros_like(X, torch::kInt32);
-        for (int i = 0; i < X.size(0); ++i) {
-            auto Xt = vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));
-            auto Xd = discretizers[features[i]]->transform(Xt);
-            Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));
-        }
-        cout << "TANNew Xtd: " << Xtd.sizes() << endl;
-        return TAN::predict(Xtd);
+        auto Xt = prepareX(X);
+        return TAN::predict(Xt);
     }
     vector<string> TANNew::graph(const string& name)
     {
diff --git a/src/Platform/Experiment.cc b/src/Platform/Experiment.cc
index 261b1c5..a79216c 100644
--- a/src/Platform/Experiment.cc
+++ b/src/Platform/Experiment.cc
@@ -146,11 +146,6 @@ namespace platform {
                 auto y_test = y.index({ test_t });
                 cout << nfold + 1 << ", " << flush;
                 clf->fit(X_train, y_train, features, className, states);
-                cout << endl;
-                auto lines = clf->show();
-                for (auto line : lines) {
-                    cout << line << endl;
-                }
                 nodes[item] = clf->getNumberOfNodes();
                 edges[item] = clf->getNumberOfEdges();
                 num_states[item] = clf->getNumberOfStates();