diff --git a/src/BayesNet/AODELd.cc b/src/BayesNet/AODELd.cc
index 8a656cc..fbfaefd 100644
--- a/src/BayesNet/AODELd.cc
+++ b/src/BayesNet/AODELd.cc
@@ -22,22 +22,15 @@ namespace bayesnet {
     void AODELd::buildModel()
     {
         models.clear();
-        cout << "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaah!" << endl;
         for (int i = 0; i < features.size(); ++i) {
-            models.push_back(Models::instance().create("SPODELd"));
-            models[i]->test();
+            models.push_back(std::make_unique<SPODELd>(i));
         }
         n_models = models.size();
     }
     void AODELd::trainModel()
     {
-        cout << "dataset: " << dataset.sizes() << endl;
-        cout << "features: " << features.size() << endl;
-        cout << "className: " << className << endl;
-        cout << "states: " << states.size() << endl;
         for (const auto& model : models) {
-            model->fit(dataset, features, className, states);
-            model->test();
+            model->fit(Xf, y, features, className, states);
         }
     }
     vector<string> AODELd::graph(const string& name) const
diff --git a/src/BayesNet/SPODELd.cc b/src/BayesNet/SPODELd.cc
index 8c47df1..0e58775 100644
--- a/src/BayesNet/SPODELd.cc
+++ b/src/BayesNet/SPODELd.cc
@@ -2,11 +2,10 @@
 
 namespace bayesnet {
     using namespace std;
-    SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) { cout << "SPODELd constructor" << endl; }
+    SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
     SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
     {
         // This first part should go in a Classifier method called fit_local_discretization o fit_float...
-        cout << "YOOOOOOOOOOOOOOOOOOOo" << endl;
         features = features_;
         className = className_;
         Xf = X_;
@@ -19,15 +18,28 @@ namespace bayesnet {
         localDiscretizationProposal(states, model);
         return *this;
     }
+    SPODELd& SPODELd::fit(torch::Tensor& dataset, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    {
+        Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
+        cout << "Xf " << Xf.sizes() << " dtype: " << Xf.dtype() << endl;
+        y = dataset.index({ -1, "..." }).clone();
+        // This first part should go in a Classifier method called fit_local_discretization o fit_float...
+        features = features_;
+        className = className_;
+        // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
+        fit_local_discretization(states, y);
+        // We have discretized the input data
+        // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
+        SPODE::fit(dataset, features, className, states);
+        localDiscretizationProposal(states, model);
+        return *this;
+    }
+
     Tensor SPODELd::predict(Tensor& X)
     {
         auto Xt = prepareX(X);
         return SPODE::predict(Xt);
     }
-    void SPODELd::test()
-    {
-        cout << "SPODELd test" << endl;
-    }
     vector<string> SPODELd::graph(const string& name) const
     {
         return SPODE::graph(name);
diff --git a/src/BayesNet/SPODELd.h b/src/BayesNet/SPODELd.h
index f949c09..b94fc6c 100644
--- a/src/BayesNet/SPODELd.h
+++ b/src/BayesNet/SPODELd.h
@@ -7,10 +7,10 @@ namespace bayesnet {
     using namespace std;
     class SPODELd : public SPODE, public Proposal {
     public:
-        void test();
         explicit SPODELd(int root);
         virtual ~SPODELd() = default;
         SPODELd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        SPODELd& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) override;
         vector<string> graph(const string& name = "SPODE") const override;
         Tensor predict(Tensor& X) override;
         static inline string version() { return "0.0.1"; };