diff --git a/.vscode/launch.json b/.vscode/launch.json
index 7241ae2..ba01ca6 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -25,7 +25,7 @@
             "program": "${workspaceFolder}/build/src/Platform/main",
             "args": [
                 "-m",
-                "AODELd",
+                "SPODELd",
                 "-p",
                 "/Users/rmontanana/Code/discretizbench/datasets",
                 "--stratified",
diff --git a/src/BayesNet/AODE.cc b/src/BayesNet/AODE.cc
index 6ec9361..7e6a95f 100644
--- a/src/BayesNet/AODE.cc
+++ b/src/BayesNet/AODE.cc
@@ -2,14 +2,14 @@
 
 namespace bayesnet {
     AODE::AODE() : Ensemble() {}
-    void AODE::train()
+    void AODE::buildModel()
     {
         models.clear();
         for (int i = 0; i < features.size(); ++i) {
             models.push_back(std::make_unique<SPODE>(i));
         }
     }
-    vector<string> AODE::graph(const string& title)
+    vector<string> AODE::graph(const string& title) const
     {
         return Ensemble::graph(title);
     }
diff --git a/src/BayesNet/AODE.h b/src/BayesNet/AODE.h
index 9a698e1..3d58851 100644
--- a/src/BayesNet/AODE.h
+++ b/src/BayesNet/AODE.h
@@ -5,11 +5,11 @@
 namespace bayesnet {
     class AODE : public Ensemble {
     protected:
-        void train() override;
+        void buildModel() override;
     public:
         AODE();
         virtual ~AODE() {};
-        vector<string> graph(const string& title = "AODE") override;
+        vector<string> graph(const string& title = "AODE") const override;
     };
 }
 #endif
\ No newline at end of file
diff --git a/src/BayesNet/AODELd.cc b/src/BayesNet/AODELd.cc
index 861aa71..9f36ed2 100644
--- a/src/BayesNet/AODELd.cc
+++ b/src/BayesNet/AODELd.cc
@@ -1,33 +1,39 @@
 #include "AODELd.h"
+#include "Models.h"
 
 namespace bayesnet {
     using namespace std;
-    AODELd::AODELd() : Ensemble(), Proposal(Ensemble::Xv, Ensemble::yv, features, className) {}
+    AODELd::AODELd() : Ensemble(), Proposal(dataset, features, className) {}
     AODELd& AODELd::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...
         features = features_;
         className = className_;
-        states = states_;
-        train();
-        for (const auto& model : models) {
-            model->fit(X_, y_, features_, className_, states_);
-        }
-        n_models = models.size();
-        fitted = true;
+        Xf = X_;
+        y = y_;
+        // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
+        states = fit_local_discretization(y);
+        // 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
+        Ensemble::fit(dataset, features, className, states);
         return *this;
+
     }
-    void AODELd::train()
+    void AODELd::buildModel()
     {
         models.clear();
         for (int i = 0; i < features.size(); ++i) {
             models.push_back(std::make_unique<SPODELd>(i));
         }
+        n_models = models.size();
     }
-    Tensor AODELd::predict(Tensor& X)
+    void AODELd::trainModel()
     {
-        return Ensemble::predict(X);
+        for (const auto& model : models) {
+            model->fit(Xf, y, features, className, states);
+        }
     }
-    vector<string> AODELd::graph(const string& name)
+    vector<string> AODELd::graph(const string& name) const
     {
         return Ensemble::graph(name);
     }
diff --git a/src/BayesNet/AODELd.h b/src/BayesNet/AODELd.h
index 33a0dff..14be0c4 100644
--- a/src/BayesNet/AODELd.h
+++ b/src/BayesNet/AODELd.h
@@ -7,13 +7,14 @@
 namespace bayesnet {
     using namespace std;
     class AODELd : public Ensemble, public Proposal {
+    protected:
+        void trainModel() override;
+        void buildModel() override;
     public:
         AODELd();
+        AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_) override;
         virtual ~AODELd() = default;
-        AODELd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
-        vector<string> graph(const string& name = "AODE") override;
-        Tensor predict(Tensor& X) override;
-        void train() override;
+        vector<string> graph(const string& name = "AODE") const override;
         static inline string version() { return "0.0.1"; };
     };
 }
diff --git a/src/BayesNet/BaseClassifier.h b/src/BayesNet/BaseClassifier.h
index 0ae9a1d..ff202e1 100644
--- a/src/BayesNet/BaseClassifier.h
+++ b/src/BayesNet/BaseClassifier.h
@@ -5,24 +5,27 @@
 namespace bayesnet {
     using namespace std;
     class BaseClassifier {
+    protected:
+        virtual void trainModel() = 0;
     public:
         // X is nxm vector, y is nx1 vector
         virtual BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
         // X is nxm tensor, y is nx1 tensor
         virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
+        virtual BaseClassifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) = 0;
         virtual ~BaseClassifier() = default;
         torch::Tensor virtual predict(torch::Tensor& X) = 0;
         vector<int> virtual predict(vector<vector<int>>& X) = 0;
         float virtual score(vector<vector<int>>& X, vector<int>& y) = 0;
         float virtual score(torch::Tensor& X, torch::Tensor& y) = 0;
-        int virtual getNumberOfNodes() = 0;
-        int virtual getNumberOfEdges() = 0;
-        int virtual getNumberOfStates() = 0;
-        vector<string> virtual show() = 0;
-        vector<string> virtual graph(const string& title = "") = 0;
+        int virtual getNumberOfNodes()const = 0;
+        int virtual getNumberOfEdges()const = 0;
+        int virtual getNumberOfStates() const = 0;
+        vector<string> virtual show() const = 0;
+        vector<string> virtual graph(const string& title = "") const = 0;
         const string inline getVersion() const { return "0.1.0"; };
         vector<string> virtual topological_order() = 0;
-        void virtual dump_cpt() = 0;
+        void virtual dump_cpt()const = 0;
     };
 }
 #endif
\ No newline at end of file
diff --git a/src/BayesNet/BayesMetrics.cc b/src/BayesNet/BayesMetrics.cc
index c80fb65..8952ead 100644
--- a/src/BayesNet/BayesMetrics.cc
+++ b/src/BayesNet/BayesMetrics.cc
@@ -2,7 +2,7 @@
 #include "Mst.h"
 namespace bayesnet {
     //samples is nxm tensor used to fit the model
-    Metrics::Metrics(torch::Tensor& samples, vector<string>& features, string& className, int classNumStates)
+    Metrics::Metrics(const torch::Tensor& samples, const vector<string>& features, const string& className, const int classNumStates)
         : samples(samples)
         , features(features)
         , className(className)
@@ -76,7 +76,7 @@ namespace bayesnet {
         std::vector<float> v(matrix.data_ptr<float>(), matrix.data_ptr<float>() + matrix.numel());
         return v;
     }
-    double Metrics::entropy(torch::Tensor& feature)
+    double Metrics::entropy(const torch::Tensor& feature)
     {
         torch::Tensor counts = feature.bincount();
         int totalWeight = counts.sum().item<int>();
@@ -86,7 +86,7 @@ namespace bayesnet {
         return entropy.nansum().item<double>();
     }
     // H(Y|X) = sum_{x in X} p(x) H(Y|X=x)
-    double Metrics::conditionalEntropy(torch::Tensor& firstFeature, torch::Tensor& secondFeature)
+    double Metrics::conditionalEntropy(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature)
     {
         int numSamples = firstFeature.sizes()[0];
         torch::Tensor featureCounts = secondFeature.bincount();
@@ -115,7 +115,7 @@ namespace bayesnet {
         return entropyValue;
     }
     // I(X;Y) = H(Y) - H(Y|X)
-    double Metrics::mutualInformation(torch::Tensor& firstFeature, torch::Tensor& secondFeature)
+    double Metrics::mutualInformation(const torch::Tensor& firstFeature, const torch::Tensor& secondFeature)
     {
         return entropy(firstFeature) - conditionalEntropy(firstFeature, secondFeature);
     }
@@ -124,7 +124,7 @@ namespace bayesnet {
     and the indices of the weights as nodes of this square matrix using
     Kruskal algorithm
     */
-    vector<pair<int, int>> Metrics::maximumSpanningTree(vector<string> features, Tensor& weights, int root)
+    vector<pair<int, int>> Metrics::maximumSpanningTree(const vector<string>& features, const Tensor& weights, const int root)
     {
         auto mst = MST(features, weights, root);
         return mst.maximumSpanningTree();
diff --git a/src/BayesNet/BayesMetrics.h b/src/BayesNet/BayesMetrics.h
index 183cfcc..2a2fff3 100644
--- a/src/BayesNet/BayesMetrics.h
+++ b/src/BayesNet/BayesMetrics.h
@@ -14,15 +14,15 @@ namespace bayesnet {
         int classNumStates = 0;
     public:
         Metrics() = default;
-        Metrics(Tensor&, vector<string>&, string&, int);
+        Metrics(const Tensor&, const vector<string>&, const string&, const int);
         Metrics(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&, const int);
-        double entropy(Tensor&);
-        double conditionalEntropy(Tensor&, Tensor&);
-        double mutualInformation(Tensor&, Tensor&);
+        double entropy(const Tensor&);
+        double conditionalEntropy(const Tensor&, const Tensor&);
+        double mutualInformation(const Tensor&, const Tensor&);
         vector<float> conditionalEdgeWeights(); // To use in Python
         Tensor conditionalEdge();
         vector<pair<string, string>> doCombinations(const vector<string>&);
-        vector<pair<int, int>> maximumSpanningTree(vector<string> features, Tensor& weights, int root);
+        vector<pair<int, int>> maximumSpanningTree(const vector<string>& features, const Tensor& weights, const int root);
     };
 }
 #endif
\ No newline at end of file
diff --git a/src/BayesNet/CMakeLists.txt b/src/BayesNet/CMakeLists.txt
index 5df47d5..a2b9126 100644
--- a/src/BayesNet/CMakeLists.txt
+++ b/src/BayesNet/CMakeLists.txt
@@ -1,5 +1,7 @@
 include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
 include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
+include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
+include_directories(${BayesNet_SOURCE_DIR}/src/Platform)
 add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc 
-    KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc AODELd.cc Mst.cc Proposal.cc)
+    KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc AODELd.cc Mst.cc Proposal.cc ${BayesNet_SOURCE_DIR}/src/Platform/Models.cc)
 target_link_libraries(BayesNet mdlp ArffFiles "${TORCH_LIBRARIES}")
\ No newline at end of file
diff --git a/src/BayesNet/Classifier.cc b/src/BayesNet/Classifier.cc
index 4d9a4c7..b3317f4 100644
--- a/src/BayesNet/Classifier.cc
+++ b/src/BayesNet/Classifier.cc
@@ -7,61 +7,65 @@ namespace bayesnet {
     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
     Classifier& Classifier::build(vector<string>& features, string className, map<string, vector<int>>& states)
     {
-        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
-        samples = torch::cat({ X, ytmp }, 0);
         this->features = features;
         this->className = className;
         this->states = states;
+        m = dataset.size(1);
+        n = dataset.size(0) - 1;
         checkFitParameters();
         auto n_classes = states[className].size();
-        metrics = Metrics(samples, features, className, n_classes);
+        metrics = Metrics(dataset, features, className, n_classes);
         model.initialize();
-        train();
-        if (Xv.empty()) {
-            // fit with tensors
-            model.fit(X, y, features, className);
-        } else {
-            // fit with vectors
-            model.fit(Xv, yv, features, className);
-        }
+        buildModel();
+        trainModel();
         fitted = true;
         return *this;
     }
+
+    void Classifier::buildDataset(Tensor& ytmp)
+    {
+        try {
+            auto yresized = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);
+            dataset = torch::cat({ dataset, yresized }, 0);
+        }
+        catch (const std::exception& e) {
+            std::cerr << e.what() << '\n';
+            cout << "X dimensions: " << dataset.sizes() << "\n";
+            cout << "y dimensions: " << ytmp.sizes() << "\n";
+            exit(1);
+        }
+    }
+    void Classifier::trainModel()
+    {
+        model.fit(dataset, features, className, states);
+    }
     // X is nxm where n is the number of features and m the number of samples
     Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
     {
-        this->X = X;
-        this->y = y;
-        Xv = vector<vector<int>>();
-        yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
+        dataset = X;
+        buildDataset(y);
         return build(features, className, states);
     }
-    void Classifier::generateTensorXFromVector()
-    {
-        X = torch::zeros({ static_cast<int>(Xv.size()), static_cast<int>(Xv[0].size()) }, kInt32);
-        for (int i = 0; i < Xv.size(); ++i) {
-            X.index_put_({ i, "..." }, torch::tensor(Xv[i], kInt32));
-        }
-    }
     // X is nxm where n is the number of features and m the number of samples
     Classifier& Classifier::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
     {
-        Xv = X;
-        generateTensorXFromVector();
-        this->y = torch::tensor(y, kInt32);
-        yv = y;
+        dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, kInt32);
+        for (int i = 0; i < X.size(); ++i) {
+            dataset.index_put_({ i, "..." }, torch::tensor(X[i], kInt32));
+        }
+        auto ytmp = torch::tensor(y, kInt32);
+        buildDataset(ytmp);
+        return build(features, className, states);
+    }
+    Classifier& Classifier::fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states)
+    {
+        this->dataset = dataset;
         return build(features, className, states);
     }
     void Classifier::checkFitParameters()
     {
-        auto sizes = X.sizes();
-        m = sizes[1];
-        n = sizes[0];
-        if (m != y.size(0)) {
-            throw invalid_argument("X and y must have the same number of samples");
-        }
         if (n != features.size()) {
-            throw invalid_argument("X and features must have the same number of features");
+            throw invalid_argument("X " + to_string(n) + " and features " + to_string(features.size()) + " must have the same number of features");
         }
         if (states.find(className) == states.end()) {
             throw invalid_argument("className not found in states");
@@ -108,7 +112,7 @@ namespace bayesnet {
         }
         return model.score(X, y);
     }
-    vector<string> Classifier::show()
+    vector<string> Classifier::show() const
     {
         return model.show();
     }
@@ -120,16 +124,16 @@ namespace bayesnet {
         }
         model.addNode(className);
     }
-    int Classifier::getNumberOfNodes()
+    int Classifier::getNumberOfNodes() const
     {
         // Features does not include class
         return fitted ? model.getFeatures().size() + 1 : 0;
     }
-    int Classifier::getNumberOfEdges()
+    int Classifier::getNumberOfEdges() const
     {
-        return fitted ? model.getEdges().size() : 0;
+        return fitted ? model.getNumEdges() : 0;
     }
-    int Classifier::getNumberOfStates()
+    int Classifier::getNumberOfStates() const
     {
         return fitted ? model.getStates() : 0;
     }
@@ -137,9 +141,8 @@ namespace bayesnet {
     {
         return model.topological_sort();
     }
-    void Classifier::dump_cpt()
+    void Classifier::dump_cpt() const
     {
         model.dump_cpt();
     }
-
 }
\ No newline at end of file
diff --git a/src/BayesNet/Classifier.h b/src/BayesNet/Classifier.h
index 03c46c6..2e736a3 100644
--- a/src/BayesNet/Classifier.h
+++ b/src/BayesNet/Classifier.h
@@ -10,39 +10,37 @@ using namespace torch;
 namespace bayesnet {
     class Classifier : public BaseClassifier {
     private:
-        bool fitted;
+        void buildDataset(torch::Tensor& y);
         Classifier& build(vector<string>& features, string className, map<string, vector<int>>& states);
     protected:
+        bool fitted;
         Network model;
         int m, n; // m: number of samples, n: number of features
-        Tensor X; // nxm tensor
-        vector<vector<int>> Xv; // nxm vector
-        Tensor y;
-        vector<int> yv;
-        Tensor samples; // (n+1)xm tensor
+        Tensor dataset; // (n+1)xm tensor
         Metrics metrics;
         vector<string> features;
         string className;
         map<string, vector<int>> states;
         void checkFitParameters();
-        void generateTensorXFromVector();
-        virtual void train() = 0;
+        virtual void buildModel() = 0;
+        void trainModel() override;
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;
         Classifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
         Classifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        Classifier& fit(torch::Tensor& dataset, vector<string>& features, string className, map<string, vector<int>>& states) override;
         void addNodes();
-        int getNumberOfNodes() override;
-        int getNumberOfEdges() override;
-        int getNumberOfStates() override;
+        int getNumberOfNodes() const override;
+        int getNumberOfEdges() const override;
+        int getNumberOfStates() const override;
         Tensor predict(Tensor& X) override;
         vector<int> predict(vector<vector<int>>& X) override;
         float score(Tensor& X, Tensor& y) override;
         float score(vector<vector<int>>& X, vector<int>& y) override;
-        vector<string> show() override;
-        vector<string> topological_order() override;
-        void dump_cpt() override;
+        vector<string> show() const override;
+        vector<string> topological_order()  override;
+        void dump_cpt() const override;
     };
 }
 #endif
diff --git a/src/BayesNet/Ensemble.cc b/src/BayesNet/Ensemble.cc
index 8cbd17c..34c6894 100644
--- a/src/BayesNet/Ensemble.cc
+++ b/src/BayesNet/Ensemble.cc
@@ -3,54 +3,15 @@
 namespace bayesnet {
     using namespace torch;
 
-    Ensemble::Ensemble() : n_models(0), metrics(Metrics()), fitted(false) {}
-    Ensemble& Ensemble::build(vector<string>& features, string className, map<string, vector<int>>& states)
+    Ensemble::Ensemble() : Classifier(Network()) {}
+
+    void Ensemble::trainModel()
     {
-        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
-        samples = torch::cat({ X, ytmp }, 0);
-        this->features = features;
-        this->className = className;
-        this->states = states;
-        auto n_classes = states[className].size();
-        metrics = Metrics(samples, features, className, n_classes);
-        // Build models
-        train();
-        // Train models
         n_models = models.size();
         for (auto i = 0; i < n_models; ++i) {
-            if (Xv.empty()) {
-                // fit with tensors
-                models[i]->fit(X, y, features, className, states);
-            } else {
-                // fit with vectors
-                models[i]->fit(Xv, yv, features, className, states);
-            }
+            // fit with vectors
+            models[i]->fit(dataset, features, className, states);
         }
-        fitted = true;
-        return *this;
-    }
-    void Ensemble::generateTensorXFromVector()
-    {
-        X = torch::zeros({ static_cast<int>(Xv.size()), static_cast<int>(Xv[0].size()) }, kInt32);
-        for (int i = 0; i < Xv.size(); ++i) {
-            X.index_put_({ i, "..." }, torch::tensor(Xv[i], kInt32));
-        }
-    }
-    Ensemble& Ensemble::fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
-    {
-        this->X = X;
-        this->y = y;
-        Xv = vector<vector<int>>();
-        yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
-        return build(features, className, states);
-    }
-    Ensemble& Ensemble::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
-    {
-        Xv = X;
-        generateTensorXFromVector();
-        this->y = torch::tensor(y, kInt32);
-        yv = y;
-        return build(features, className, states);
     }
     vector<int> Ensemble::voting(Tensor& y_pred)
     {
@@ -132,9 +93,8 @@ namespace bayesnet {
             }
         }
         return (double)correct / y_pred.size();
-
     }
-    vector<string> Ensemble::show()
+    vector<string> Ensemble::show() const
     {
         auto result = vector<string>();
         for (auto i = 0; i < n_models; ++i) {
@@ -143,7 +103,7 @@ namespace bayesnet {
         }
         return result;
     }
-    vector<string> Ensemble::graph(const string& title)
+    vector<string> Ensemble::graph(const string& title) const
     {
         auto result = vector<string>();
         for (auto i = 0; i < n_models; ++i) {
@@ -152,7 +112,7 @@ namespace bayesnet {
         }
         return result;
     }
-    int Ensemble::getNumberOfNodes()
+    int Ensemble::getNumberOfNodes() const
     {
         int nodes = 0;
         for (auto i = 0; i < n_models; ++i) {
@@ -160,7 +120,7 @@ namespace bayesnet {
         }
         return nodes;
     }
-    int Ensemble::getNumberOfEdges()
+    int Ensemble::getNumberOfEdges() const
     {
         int edges = 0;
         for (auto i = 0; i < n_models; ++i) {
@@ -168,7 +128,7 @@ namespace bayesnet {
         }
         return edges;
     }
-    int Ensemble::getNumberOfStates()
+    int Ensemble::getNumberOfStates() const
     {
         int nstates = 0;
         for (auto i = 0; i < n_models; ++i) {
diff --git a/src/BayesNet/Ensemble.h b/src/BayesNet/Ensemble.h
index 322032e..f0d750b 100644
--- a/src/BayesNet/Ensemble.h
+++ b/src/BayesNet/Ensemble.h
@@ -8,44 +8,31 @@ using namespace std;
 using namespace torch;
 
 namespace bayesnet {
-    class Ensemble : public BaseClassifier {
+    class Ensemble : public Classifier {
     private:
         Ensemble& build(vector<string>& features, string className, map<string, vector<int>>& states);
     protected:
         unsigned n_models;
-        bool fitted;
         vector<unique_ptr<Classifier>> models;
-        Tensor X;
-        vector<vector<int>> Xv;
-        Tensor y;
-        vector<int> yv;
-        Tensor samples;
-        Metrics metrics;
-        vector<string> features;
-        string className;
-        map<string, vector<int>> states;
-        void virtual train() = 0;
+        void trainModel() override;
         vector<int> voting(Tensor& y_pred);
-        void generateTensorXFromVector();
     public:
         Ensemble();
         virtual ~Ensemble() = default;
-        Ensemble& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
-        Ensemble& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
         Tensor predict(Tensor& X) override;
         vector<int> predict(vector<vector<int>>& X) override;
         float score(Tensor& X, Tensor& y) override;
         float score(vector<vector<int>>& X, vector<int>& y) override;
-        int getNumberOfNodes() override;
-        int getNumberOfEdges() override;
-        int getNumberOfStates() override;
-        vector<string> show() override;
-        vector<string> graph(const string& title) override;
-        vector<string> topological_order() override
+        int getNumberOfNodes() const override;
+        int getNumberOfEdges() const override;
+        int getNumberOfStates() const override;
+        vector<string> show() const override;
+        vector<string> graph(const string& title) const override;
+        vector<string> topological_order()  override
         {
             return vector<string>();
         }
-        void dump_cpt() override
+        void dump_cpt() const override
         {
         }
     };
diff --git a/src/BayesNet/KDB.cc b/src/BayesNet/KDB.cc
index a0ab434..74566b0 100644
--- a/src/BayesNet/KDB.cc
+++ b/src/BayesNet/KDB.cc
@@ -4,7 +4,7 @@ namespace bayesnet {
     using namespace torch;
 
     KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta) {}
-    void KDB::train()
+    void KDB::buildModel()
     {
         /*
         1. For each feature Xi, compute mutual information, I(X;C),
@@ -28,9 +28,10 @@ namespace bayesnet {
         // 1. For each feature Xi, compute mutual information, I(X;C),
         // where C is the class.
         addNodes();
+        const Tensor& y = dataset.index({ -1, "..." });
         vector <float> mi;
         for (auto i = 0; i < features.size(); i++) {
-            Tensor firstFeature = X.index({ i, "..." });
+            Tensor firstFeature = dataset.index({ i, "..." });
             mi.push_back(metrics.mutualInformation(firstFeature, y));
         }
         // 2. Compute class conditional mutual information I(Xi;XjIC), f or each
@@ -78,7 +79,7 @@ namespace bayesnet {
             exit_cond = num == n_edges || candidates.size(0) == 0;
         }
     }
-    vector<string> KDB::graph(const string& title)
+    vector<string> KDB::graph(const string& title) const
     {
         string header{ title };
         if (title == "KDB") {
diff --git a/src/BayesNet/KDB.h b/src/BayesNet/KDB.h
index 11a69d7..e7af8c5 100644
--- a/src/BayesNet/KDB.h
+++ b/src/BayesNet/KDB.h
@@ -11,11 +11,11 @@ namespace bayesnet {
         float theta;
         void add_m_edges(int idx, vector<int>& S, Tensor& weights);
     protected:
-        void train() override;
+        void buildModel() override;
     public:
         explicit KDB(int k, float theta = 0.03);
         virtual ~KDB() {};
-        vector<string> graph(const string& name = "KDB") override;
+        vector<string> graph(const string& name = "KDB") const override;
     };
 }
 #endif
\ No newline at end of file
diff --git a/src/BayesNet/KDBLd.cc b/src/BayesNet/KDBLd.cc
index d2cbed4..724a053 100644
--- a/src/BayesNet/KDBLd.cc
+++ b/src/BayesNet/KDBLd.cc
@@ -2,7 +2,7 @@
 
 namespace bayesnet {
     using namespace std;
-    KDBLd::KDBLd(int k) : KDB(k), Proposal(KDB::Xv, KDB::yv, features, className) {}
+    KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
     KDBLd& KDBLd::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...
@@ -11,16 +11,11 @@ namespace bayesnet {
         Xf = X_;
         y = y_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
-        generateTensorXFromVector();
+        states = fit_local_discretization(y);
         // We have discretized the input data
         // 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);
+        KDB::fit(dataset, features, className, states);
         localDiscretizationProposal(states, model);
-        generateTensorXFromVector();
-        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
-        samples = torch::cat({ X, ytmp }, 0);
-        model.fit(KDB::Xv, KDB::yv, features, className);
         return *this;
     }
     Tensor KDBLd::predict(Tensor& X)
@@ -28,7 +23,7 @@ namespace bayesnet {
         auto Xt = prepareX(X);
         return KDB::predict(Xt);
     }
-    vector<string> KDBLd::graph(const string& name)
+    vector<string> KDBLd::graph(const string& name) const
     {
         return KDB::graph(name);
     }
diff --git a/src/BayesNet/KDBLd.h b/src/BayesNet/KDBLd.h
index b91999b..50a1b95 100644
--- a/src/BayesNet/KDBLd.h
+++ b/src/BayesNet/KDBLd.h
@@ -11,7 +11,7 @@ namespace bayesnet {
         explicit KDBLd(int k);
         virtual ~KDBLd() = default;
         KDBLd& 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;
+        vector<string> graph(const string& name = "KDB") const override;
         Tensor predict(Tensor& X) override;
         static inline string version() { return "0.0.1"; };
     };
diff --git a/src/BayesNet/Mst.cc b/src/BayesNet/Mst.cc
index 3a48d05..b915d76 100644
--- a/src/BayesNet/Mst.cc
+++ b/src/BayesNet/Mst.cc
@@ -94,7 +94,7 @@ namespace bayesnet {
         return result;
     }
 
-    MST::MST(vector<string>& features, Tensor& weights, int root) : features(features), weights(weights), root(root) {}
+    MST::MST(const vector<string>& features, const Tensor& weights, const int root) : features(features), weights(weights), root(root) {}
     vector<pair<int, int>> MST::maximumSpanningTree()
     {
         auto num_features = features.size();
diff --git a/src/BayesNet/Mst.h b/src/BayesNet/Mst.h
index 71a46a5..e0f3372 100644
--- a/src/BayesNet/Mst.h
+++ b/src/BayesNet/Mst.h
@@ -13,7 +13,7 @@ namespace bayesnet {
         int root = 0;
     public:
         MST() = default;
-        MST(vector<string>& features, Tensor& weights, int root);
+        MST(const vector<string>& features, const Tensor& weights, const int root);
         vector<pair<int, int>> maximumSpanningTree();
     };
     class Graph {
diff --git a/src/BayesNet/Network.cc b/src/BayesNet/Network.cc
index 2a37de4..8a4106c 100644
--- a/src/BayesNet/Network.cc
+++ b/src/BayesNet/Network.cc
@@ -20,7 +20,6 @@ namespace bayesnet {
         classNumStates = 0;
         fitted = false;
         nodes.clear();
-        dataset.clear();
         samples = torch::Tensor();
     }
     float Network::getmaxThreads()
@@ -44,15 +43,15 @@ namespace bayesnet {
         }
         nodes[name] = std::make_unique<Node>(name);
     }
-    vector<string> Network::getFeatures()
+    vector<string> Network::getFeatures() const
     {
         return features;
     }
-    int Network::getClassNumStates()
+    int Network::getClassNumStates() const
     {
         return classNumStates;
     }
-    int Network::getStates()
+    int Network::getStates() const
     {
         int result = 0;
         for (auto& node : nodes) {
@@ -60,7 +59,7 @@ namespace bayesnet {
         }
         return result;
     }
-    string Network::getClassName()
+    string Network::getClassName() const
     {
         return className;
     }
@@ -105,7 +104,7 @@ namespace bayesnet {
     {
         return nodes;
     }
-    void Network::checkFitData(int n_samples, int n_features, int n_samples_y, const vector<string>& featureNames, const string& className)
+    void Network::checkFitData(int n_samples, int n_features, int n_samples_y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
         if (n_samples != n_samples_y) {
             throw invalid_argument("X and y must have the same number of samples in Network::fit (" + to_string(n_samples) + " != " + to_string(n_samples_y) + ")");
@@ -123,50 +122,54 @@ namespace bayesnet {
             if (find(features.begin(), features.end(), feature) == features.end()) {
                 throw invalid_argument("Feature " + feature + " not found in Network::features");
             }
+            if (states.find(feature) == states.end()) {
+                throw invalid_argument("Feature " + feature + " not found in states");
+            }
         }
     }
-    void Network::setStates()
+    void Network::setStates(const map<string, vector<int>>& states)
     {
         // Set states to every Node in the network
         for (int i = 0; i < features.size(); ++i) {
-            nodes[features[i]]->setNumStates(static_cast<int>(torch::max(samples.index({ i, "..." })).item<int>()) + 1);
+            nodes[features[i]]->setNumStates(states.at(features[i]).size());
         }
         classNumStates = nodes[className]->getNumStates();
     }
     // X comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(torch::Tensor& X, torch::Tensor& y, const vector<string>& featureNames, const string& className)
+    void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
-        checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className);
+        checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states);
         this->className = className;
-        dataset.clear();
         Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
         samples = torch::cat({ X , ytmp }, 0);
         for (int i = 0; i < featureNames.size(); ++i) {
             auto row_feature = X.index({ i, "..." });
-            dataset[featureNames[i]] = vector<int>(row_feature.data_ptr<int>(), row_feature.data_ptr<int>() + row_feature.size(0));;
         }
-        dataset[className] = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
-        completeFit();
+        completeFit(states);
+    }
+    void Network::fit(const torch::Tensor& samples, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
+    {
+        checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states);
+        this->className = className;
+        this->samples = samples;
+        completeFit(states);
     }
     // input_data comes in nxm, where n is the number of features and m the number of samples
-    void Network::fit(const vector<vector<int>>& input_data, const vector<int>& labels, const vector<string>& featureNames, const string& className)
+    void Network::fit(const vector<vector<int>>& input_data, const vector<int>& labels, const vector<string>& featureNames, const string& className, const map<string, vector<int>>& states)
     {
-        checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className);
+        checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states);
         this->className = className;
-        dataset.clear();
-        // Build dataset & tensor of samples (nxm) (n+1 because of the class)
+        // Build tensor of samples (nxm) (n+1 because of the class)
         samples = torch::zeros({ static_cast<int>(input_data.size() + 1), static_cast<int>(input_data[0].size()) }, torch::kInt32);
         for (int i = 0; i < featureNames.size(); ++i) {
-            dataset[featureNames[i]] = input_data[i];
             samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
         }
-        dataset[className] = labels;
         samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
-        completeFit();
+        completeFit(states);
     }
-    void Network::completeFit()
+    void Network::completeFit(const map<string, vector<int>>& states)
     {
-        setStates();
+        setStates(states);
         int maxThreadsRunning = static_cast<int>(std::thread::hardware_concurrency() * maxThreads);
         if (maxThreadsRunning < 1) {
             maxThreadsRunning = 1;
@@ -188,7 +191,7 @@ namespace bayesnet {
                     auto& pair = *std::next(nodes.begin(), nextNodeIndex);
                     ++nextNodeIndex;
                     lock.unlock();
-                    pair.second->computeCPT(dataset, laplaceSmoothing);
+                    pair.second->computeCPT(samples, features, laplaceSmoothing);
                     lock.lock();
                     nodes[pair.first] = std::move(pair.second);
                     lock.unlock();
@@ -212,7 +215,7 @@ namespace bayesnet {
         torch::Tensor result;
         result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);
         for (int i = 0; i < samples.size(1); ++i) {
-            auto sample = samples.index({ "...", i });
+            const Tensor sample = samples.index({ "...", i });
             auto psample = predict_sample(sample);
             auto temp = torch::tensor(psample, torch::kFloat64);
             //            result.index_put_({ i, "..." }, torch::tensor(predict_sample(sample), torch::kFloat64));
@@ -328,12 +331,12 @@ namespace bayesnet {
         mutex mtx;
         for (int i = 0; i < classNumStates; ++i) {
             threads.emplace_back([this, &result, &evidence, i, &mtx]() {
-            auto completeEvidence = map<string, int>(evidence);
-            completeEvidence[getClassName()] = i;
+                auto completeEvidence = map<string, int>(evidence);
+                completeEvidence[getClassName()] = i;
                 double factor = computeFactor(completeEvidence);
                 lock_guard<mutex> lock(mtx);
                 result[i] = factor;
-            });
+                });
         }
         for (auto& thread : threads) {
             thread.join();
@@ -343,7 +346,7 @@ namespace bayesnet {
         transform(result.begin(), result.end(), result.begin(), [sum](double& value) { return value / sum; });
         return result;
     }
-    vector<string> Network::show()
+    vector<string> Network::show() const
     {
         vector<string> result;
         // Draw the network
@@ -356,7 +359,7 @@ namespace bayesnet {
         }
         return result;
     }
-    vector<string> Network::graph(const string& title)
+    vector<string> Network::graph(const string& title) const
     {
         auto output = vector<string>();
         auto prefix = "digraph BayesNet {\nlabel=<BayesNet ";
@@ -370,7 +373,7 @@ namespace bayesnet {
         output.push_back("}\n");
         return output;
     }
-    vector<pair<string, string>> Network::getEdges()
+    vector<pair<string, string>> Network::getEdges() const
     {
         auto edges = vector<pair<string, string>>();
         for (const auto& node : nodes) {
@@ -382,6 +385,10 @@ namespace bayesnet {
         }
         return edges;
     }
+    int Network::getNumEdges() const
+    {
+        return getEdges().size();
+    }
     vector<string> Network::topological_sort()
     {
         /* Check if al the fathers of every node are before the node */
@@ -420,7 +427,7 @@ namespace bayesnet {
         }
         return result;
     }
-    void Network::dump_cpt()
+    void Network::dump_cpt() const
     {
         for (auto& node : nodes) {
             cout << "* " << node.first << ": (" << node.second->getNumStates() << ") : " << node.second->getCPT().sizes() << endl;
diff --git a/src/BayesNet/Network.h b/src/BayesNet/Network.h
index b27125c..d8db620 100644
--- a/src/BayesNet/Network.h
+++ b/src/BayesNet/Network.h
@@ -8,11 +8,10 @@ namespace bayesnet {
     class Network {
     private:
         map<string, unique_ptr<Node>> nodes;
-        map<string, vector<int>> dataset;
         bool fitted;
         float maxThreads = 0.95;
         int classNumStates;
-        vector<string> features; // Including class
+        vector<string> features; // Including classname
         string className;
         int laplaceSmoothing = 1;
         torch::Tensor samples; // nxm tensor used to fit the model
@@ -21,13 +20,9 @@ namespace bayesnet {
         vector<double> predict_sample(const torch::Tensor&);
         vector<double> exactInference(map<string, int>&);
         double computeFactor(map<string, int>&);
-        double mutual_info(torch::Tensor&, torch::Tensor&);
-        double entropy(torch::Tensor&);
-        double conditionalEntropy(torch::Tensor&, torch::Tensor&);
-        double mutualInformation(torch::Tensor&, torch::Tensor&);
-        void completeFit();
-        void checkFitData(int n_features, int n_samples, int n_samples_y, const vector<string>& featureNames, const string& className);
-        void setStates();
+        void completeFit(const map<string, vector<int>>&);
+        void checkFitData(int n_features, int n_samples, int n_samples_y, const vector<string>& featureNames, const string& className, const map<string, vector<int>>&);
+        void setStates(const map<string, vector<int>>&);
     public:
         Network();
         explicit Network(float, int);
@@ -38,26 +33,26 @@ namespace bayesnet {
         void addNode(const string&);
         void addEdge(const string&, const string&);
         map<string, std::unique_ptr<Node>>& getNodes();
-        vector<string> getFeatures();
-        int getStates();
-        vector<pair<string, string>> getEdges();
-        int getClassNumStates();
-        string getClassName();
-        void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&);
-        void fit(torch::Tensor&, torch::Tensor&, const vector<string>&, const string&);
+        vector<string> getFeatures() const;
+        int getStates() const;
+        vector<pair<string, string>> getEdges() const;
+        int getNumEdges() const;
+        int getClassNumStates() const;
+        string getClassName() const;
+        void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&, const map<string, vector<int>>&);
+        void fit(const torch::Tensor&, const torch::Tensor&, const vector<string>&, const string&, const map<string, vector<int>>&);
+        void fit(const torch::Tensor&, const vector<string>&, const string&, const map<string, vector<int>>&);
         vector<int> predict(const vector<vector<int>>&); // Return mx1 vector of predictions
         torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
-        //Computes the conditional edge weight of variable index u and v conditioned on class_node
-        torch::Tensor conditionalEdgeWeight();
         torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);
         vector<vector<double>> predict_proba(const vector<vector<int>>&); // Return mxn vector of probabilities
         torch::Tensor predict_proba(const torch::Tensor&); // Return mxn tensor of probabilities
         double score(const vector<vector<int>>&, const vector<int>&);
         vector<string> topological_sort();
-        vector<string> show();
-        vector<string> graph(const string& title); // Returns a vector of strings representing the graph in graphviz format
+        vector<string> show() const;
+        vector<string> graph(const string& title) const; // Returns a vector of strings representing the graph in graphviz format
         void initialize();
-        void dump_cpt();
+        void dump_cpt() const;
         inline string version() { return "0.1.0"; }
     };
 }
diff --git a/src/BayesNet/Node.cc b/src/BayesNet/Node.cc
index 3fca064..6669819 100644
--- a/src/BayesNet/Node.cc
+++ b/src/BayesNet/Node.cc
@@ -84,7 +84,7 @@ namespace bayesnet {
         }
         return result;
     }
-    void Node::computeCPT(map<string, vector<int>>& dataset, const int laplaceSmoothing)
+    void Node::computeCPT(const torch::Tensor& dataset, const vector<string>& features, const int laplaceSmoothing)
     {
         dimensions.clear();
         // Get dimensions of the CPT
@@ -94,10 +94,22 @@ namespace bayesnet {
         // Create a tensor of zeros with the dimensions of the CPT
         cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
         // Fill table with counts
-        for (int n_sample = 0; n_sample < dataset[name].size(); ++n_sample) {
+        auto pos = find(features.begin(), features.end(), name);
+        if (pos == features.end()) {
+            throw logic_error("Feature " + name + " not found in dataset");
+        }
+        int name_index = pos - features.begin();
+        for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
             torch::List<c10::optional<torch::Tensor>> coordinates;
-            coordinates.push_back(torch::tensor(dataset[name][n_sample]));
-            transform(parents.begin(), parents.end(), back_inserter(coordinates), [&dataset, &n_sample](const auto& parent) { return torch::tensor(dataset[parent->getName()][n_sample]); });
+            coordinates.push_back(dataset.index({ name_index, n_sample }));
+            for (auto parent : parents) {
+                pos = find(features.begin(), features.end(), parent->getName());
+                if (pos == features.end()) {
+                    throw logic_error("Feature parent " + parent->getName() + " not found in dataset");
+                }
+                int parent_index = pos - features.begin();
+                coordinates.push_back(dataset.index({ parent_index, n_sample }));
+            }
             // Increment the count of the corresponding coordinate
             cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + 1);
         }
diff --git a/src/BayesNet/Node.h b/src/BayesNet/Node.h
index b923dec..f4eb320 100644
--- a/src/BayesNet/Node.h
+++ b/src/BayesNet/Node.h
@@ -26,7 +26,7 @@ namespace bayesnet {
         vector<Node*>& getParents();
         vector<Node*>& getChildren();
         torch::Tensor& getCPT();
-        void computeCPT(map<string, vector<int>>&, const int);
+        void computeCPT(const torch::Tensor&, const vector<string>&, const int);
         int getNumStates() const;
         void setNumStates(int);
         unsigned minFill();
diff --git a/src/BayesNet/Proposal.cc b/src/BayesNet/Proposal.cc
index c1d3626..d53094d 100644
--- a/src/BayesNet/Proposal.cc
+++ b/src/BayesNet/Proposal.cc
@@ -2,7 +2,7 @@
 #include "ArffFiles.h"
 
 namespace bayesnet {
-    Proposal::Proposal(vector<vector<int>>& Xv_, vector<int>& yv_, vector<string>& features_, string& className_) : Xv(Xv_), yv(yv_), pFeatures(features_), pClassName(className_) {}
+    Proposal::Proposal(torch::Tensor& dataset_, vector<string>& features_, string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}
     Proposal::~Proposal()
     {
         for (auto& [key, value] : discretizers) {
@@ -16,7 +16,6 @@ namespace bayesnet {
         auto order = model.topological_sort();
         auto& nodes = model.getNodes();
         vector<int> indicesToReDiscretize;
-        auto n_samples = Xf.size(1);
         bool upgrade = false; // Flag to check if we need to upgrade the model
         for (auto feature : order) {
             auto nodeParents = nodes[feature]->getParents();
@@ -30,13 +29,13 @@ namespace bayesnet {
             parents.erase(remove(parents.begin(), parents.end(), pClassName), parents.end());
             // Get the indices of the parents
             vector<int> indices;
+            indices.push_back(-1); // Add class index
             transform(parents.begin(), parents.end(), back_inserter(indices), [&](const auto& p) {return find(pFeatures.begin(), pFeatures.end(), p) - pFeatures.begin(); });
             // Now we fit the discretizer of the feature, conditioned on its parents and the class i.e. discretizer.fit(X[index], X[indices] + y)
-            vector<string> yJoinParents;
-            transform(yv.begin(), yv.end(), back_inserter(yJoinParents), [&](const auto& p) {return to_string(p); });
+            vector<string> yJoinParents(Xf.size(1));
             for (auto idx : indices) {
-                for (int i = 0; i < n_samples; ++i) {
-                    yJoinParents[i] += to_string(Xv[idx][i]);
+                for (int i = 0; i < Xf.size(1); ++i) {
+                    yJoinParents[i] += to_string(pDataset.index({ idx, i }).item<int>());
                 }
             }
             auto arff = ArffFiles();
@@ -59,26 +58,30 @@ namespace bayesnet {
             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);
+                pDataset.index_put_({ index, "..." }, torch::tensor(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;
             }
+            model.fit(pDataset, pFeatures, pClassName, states);
         }
     }
-    void Proposal::fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y)
+    map<string, vector<int>> Proposal::fit_local_discretization(torch::Tensor& y)
     {
-        // Sharing Xv and yv with Classifier
-        Xv = vector<vector<int>>();
-        yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
+        // Discretize the continuous input data and build pDataset (Classifier::dataset)
+        int m = Xf.size(1);
+        int n = Xf.size(0);
+        map<string, vector<int>> states;
+        pDataset = torch::zeros({ n + 1, m }, kInt32);
+        auto yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
         // discretize input data by feature(row)
-        for (int i = 0; i < pFeatures.size(); ++i) {
+        for (auto i = 0; i < pFeatures.size(); ++i) {
             auto* discretizer = new mdlp::CPPFImdlp();
             auto Xt_ptr = Xf.index({ i }).data_ptr<float>();
             auto Xt = vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
             discretizer->fit(Xt, yv);
-            Xv.push_back(discretizer->transform(Xt));
+            pDataset.index_put_({ i, "..." }, torch::tensor(discretizer->transform(Xt)));
             auto xStates = vector<int>(discretizer->getCutPoints().size() + 1);
             iota(xStates.begin(), xStates.end(), 0);
             states[pFeatures[i]] = xStates;
@@ -88,6 +91,8 @@ namespace bayesnet {
         auto yStates = vector<int>(n_classes);
         iota(yStates.begin(), yStates.end(), 0);
         states[pClassName] = yStates;
+        pDataset.index_put_({ n, "..." }, y);
+        return states;
     }
     torch::Tensor Proposal::prepareX(torch::Tensor& X)
     {
diff --git a/src/BayesNet/Proposal.h b/src/BayesNet/Proposal.h
index a5650b4..10814c2 100644
--- a/src/BayesNet/Proposal.h
+++ b/src/BayesNet/Proposal.h
@@ -10,20 +10,20 @@
 namespace bayesnet {
     class Proposal {
     public:
-        Proposal(vector<vector<int>>& Xv_, vector<int>& yv_, vector<string>& features_, string& className_);
+        Proposal(torch::Tensor& pDataset, 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);
+        map<string, vector<int>> fit_local_discretization(torch::Tensor& y);
         torch::Tensor Xf; // X continuous nxm tensor
+        torch::Tensor y; // y discrete nx1 tensor
         map<string, mdlp::CPPFImdlp*> discretizers;
     private:
+        torch::Tensor& pDataset; // (n+1)xm tensor
         vector<string>& pFeatures;
         string& pClassName;
-        vector<vector<int>>& Xv; // X discrete nxm vector
-        vector<int>& yv;
     };
 }
 
-#endif
\ No newline at end of file
+#endif  
\ No newline at end of file
diff --git a/src/BayesNet/SPODE.cc b/src/BayesNet/SPODE.cc
index a627cca..a90e5ef 100644
--- a/src/BayesNet/SPODE.cc
+++ b/src/BayesNet/SPODE.cc
@@ -4,7 +4,7 @@ namespace bayesnet {
 
     SPODE::SPODE(int root) : Classifier(Network()), root(root) {}
 
-    void SPODE::train()
+    void SPODE::buildModel()
     {
         // 0. Add all nodes to the model
         addNodes();
@@ -17,7 +17,7 @@ namespace bayesnet {
             }
         }
     }
-    vector<string> SPODE::graph(const string& name)
+    vector<string> SPODE::graph(const string& name) const
     {
         return model.graph(name);
     }
diff --git a/src/BayesNet/SPODE.h b/src/BayesNet/SPODE.h
index 4625714..f9b6af0 100644
--- a/src/BayesNet/SPODE.h
+++ b/src/BayesNet/SPODE.h
@@ -7,11 +7,11 @@ namespace bayesnet {
     private:
         int root;
     protected:
-        void train() override;
+        void buildModel() override;
     public:
         explicit SPODE(int root);
         virtual ~SPODE() {};
-        vector<string> graph(const string& name = "SPODE") override;
+        vector<string> graph(const string& name = "SPODE") const override;
     };
 }
 #endif
\ No newline at end of file
diff --git a/src/BayesNet/SPODELd.cc b/src/BayesNet/SPODELd.cc
index f7df9b6..9683b7e 100644
--- a/src/BayesNet/SPODELd.cc
+++ b/src/BayesNet/SPODELd.cc
@@ -2,7 +2,7 @@
 
 namespace bayesnet {
     using namespace std;
-    SPODELd::SPODELd(int root) : SPODE(root), Proposal(SPODE::Xv, SPODE::yv, features, className) {}
+    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...
@@ -11,24 +11,36 @@ namespace bayesnet {
         Xf = X_;
         y = y_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
-        generateTensorXFromVector();
+        states = fit_local_discretization(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(SPODE::Xv, SPODE::yv, features, className, states);
+        SPODE::fit(dataset, features, className, states);
         localDiscretizationProposal(states, model);
-        generateTensorXFromVector();
-        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
-        samples = torch::cat({ X, ytmp }, 0);
-        model.fit(SPODE::Xv, SPODE::yv, features, className);
         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
+        states = fit_local_discretization(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);
     }
-    vector<string> SPODELd::graph(const string& name)
+    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 789af7f..b94fc6c 100644
--- a/src/BayesNet/SPODELd.h
+++ b/src/BayesNet/SPODELd.h
@@ -6,12 +6,12 @@
 namespace bayesnet {
     using namespace std;
     class SPODELd : public SPODE, public Proposal {
-    private:
     public:
         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;
-        vector<string> graph(const string& name = "SPODE") 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"; };
     };
diff --git a/src/BayesNet/TAN.cc b/src/BayesNet/TAN.cc
index f47d7c5..7b3e3a6 100644
--- a/src/BayesNet/TAN.cc
+++ b/src/BayesNet/TAN.cc
@@ -5,16 +5,16 @@ namespace bayesnet {
 
     TAN::TAN() : Classifier(Network()) {}
 
-    void TAN::train()
+    void TAN::buildModel()
     {
         // 0. Add all nodes to the model
         addNodes();
         // 1. Compute mutual information between each feature and the class and set the root node
         // as the highest mutual information with the class
         auto mi = vector <pair<int, float >>();
-        Tensor class_dataset = samples.index({ -1, "..." });
+        Tensor class_dataset = dataset.index({ -1, "..." });
         for (int i = 0; i < static_cast<int>(features.size()); ++i) {
-            Tensor feature_dataset = samples.index({ i, "..." });
+            Tensor feature_dataset = dataset.index({ i, "..." });
             auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset);
             mi.push_back({ i, mi_value });
         }
@@ -34,7 +34,7 @@ namespace bayesnet {
             model.addEdge(className, feature);
         }
     }
-    vector<string> TAN::graph(const string& title)
+    vector<string> TAN::graph(const string& title) const
     {
         return model.graph(title);
     }
diff --git a/src/BayesNet/TAN.h b/src/BayesNet/TAN.h
index 5c7cf49..4c1c5f5 100644
--- a/src/BayesNet/TAN.h
+++ b/src/BayesNet/TAN.h
@@ -7,11 +7,11 @@ namespace bayesnet {
     class TAN : public Classifier {
     private:
     protected:
-        void train() override;
+        void buildModel() override;
     public:
         TAN();
         virtual ~TAN() {};
-        vector<string> graph(const string& name = "TAN") override;
+        vector<string> graph(const string& name = "TAN") const override;
     };
 }
 #endif
\ No newline at end of file
diff --git a/src/BayesNet/TANLd.cc b/src/BayesNet/TANLd.cc
index f0fe110..a30cba8 100644
--- a/src/BayesNet/TANLd.cc
+++ b/src/BayesNet/TANLd.cc
@@ -2,7 +2,7 @@
 
 namespace bayesnet {
     using namespace std;
-    TANLd::TANLd() : TAN(), Proposal(TAN::Xv, TAN::yv, features, className) {}
+    TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
     TANLd& TANLd::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...
@@ -11,24 +11,20 @@ namespace bayesnet {
         Xf = X_;
         y = y_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
-        generateTensorXFromVector();
+        states = fit_local_discretization(y);
         // 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
-        TAN::fit(TAN::Xv, TAN::yv, features, className, states);
+        TAN::fit(dataset, features, className, states);
         localDiscretizationProposal(states, model);
-        generateTensorXFromVector();
-        Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
-        samples = torch::cat({ X, ytmp }, 0);
-        model.fit(TAN::Xv, TAN::yv, features, className);
         return *this;
+
     }
     Tensor TANLd::predict(Tensor& X)
     {
         auto Xt = prepareX(X);
         return TAN::predict(Xt);
     }
-    vector<string> TANLd::graph(const string& name)
+    vector<string> TANLd::graph(const string& name) const
     {
         return TAN::graph(name);
     }
diff --git a/src/BayesNet/TANLd.h b/src/BayesNet/TANLd.h
index d9172ac..c35e843 100644
--- a/src/BayesNet/TANLd.h
+++ b/src/BayesNet/TANLd.h
@@ -11,7 +11,7 @@ namespace bayesnet {
         TANLd();
         virtual ~TANLd() = default;
         TANLd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
-        vector<string> graph(const string& name = "TAN") override;
+        vector<string> graph(const string& name = "TAN") const override;
         Tensor predict(Tensor& X) override;
         static inline string version() { return "0.0.1"; };
     };
diff --git a/src/Platform/Report.cc b/src/Platform/Report.cc
index 90aad2b..3693248 100644
--- a/src/Platform/Report.cc
+++ b/src/Platform/Report.cc
@@ -4,6 +4,7 @@ namespace platform {
     string headerLine(const string& text)
     {
         int n = MAXL - text.length() - 3;
+        n = n < 0 ? 0 : n;
         return "* " + text + string(n, ' ') + "*\n";
     }
     string Report::fromVector(const string& key)
@@ -13,7 +14,7 @@ namespace platform {
         for (auto& item : data[key]) {
             result += to_string(item) + ", ";
         }
-        return "[" + result.substr(0, result.length() - 2) + "]";
+        return "[" + result.substr(0, result.size() - 2) + "]";
     }
     string fVector(const json& data)
     {
@@ -21,7 +22,7 @@ namespace platform {
         for (const auto& item : data) {
             result += to_string(item) + ", ";
         }
-        return "[" + result.substr(0, result.length() - 2) + "]";
+        return "[" + result.substr(0, result.size() - 2) + "]";
     }
     void Report::show()
     {