diff --git a/src/experimental_clfs/XA1DE.cpp b/src/experimental_clfs/XA1DE.cpp
index 72d2ac2..1ff388b 100644
--- a/src/experimental_clfs/XA1DE.cpp
+++ b/src/experimental_clfs/XA1DE.cpp
@@ -22,22 +22,21 @@ namespace platform {
             throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
         }
     }
-    void XA1DE::fit(std::vector<std::vector<int>> X, std::vector<int> y, std::vector<double> weights)
+    XA1DE& XA1DE::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
     {
         Timer timer, timert;
         timer.start();
         timert.start();
-        weights_ = weights;
         std::vector<std::vector<int>> instances = X;
         instances.push_back(y);
         int num_instances = instances[0].size();
         int num_attributes = instances.size();
         normalize_weights(num_instances);
-        std::vector<int> states;
+        std::vector<int> statesv;
         for (int i = 0; i < num_attributes; i++) {
-            states.push_back(*max_element(instances[i].begin(), instances[i].end()) + 1);
+            statesv.push_back(*max_element(instances[i].begin(), instances[i].end()) + 1);
         }
-        aode_.init(states);
+        aode_.init(statesv);
         aode_.duration_first += timer.getDuration(); timer.start();
         std::vector<int> instance;
         for (int n_instance = 0; n_instance < num_instances; n_instance++) {
@@ -62,6 +61,7 @@ namespace platform {
             std::cout << "* Time to build the model: " << timert.getDuration() << " seconds" << std::endl;
             // exit(1);
         }
+        return *this;
     }
     std::vector<std::vector<double>> XA1DE::predict_proba(std::vector<std::vector<int>>& test_data)
     {
diff --git a/src/experimental_clfs/XA1DE.h b/src/experimental_clfs/XA1DE.h
index 6839f96..2f7c59f 100644
--- a/src/experimental_clfs/XA1DE.h
+++ b/src/experimental_clfs/XA1DE.h
@@ -21,34 +21,34 @@ namespace platform {
     public:
         XA1DE();
         virtual ~XA1DE() = default;
-        void setDebug(bool debug) { this->debug = debug; }
-        std::vector<std::vector<double>> predict_proba_threads(const std::vector<std::vector<int>>& test_data);
 
+        std::vector<std::vector<double>> predict_proba_threads(const std::vector<std::vector<int>>& test_data);
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
+        float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
         XA1DE& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
-        XA1DE& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
-        XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
-        XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
+        XA1DE& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override { return *this; };
+        XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override { return *this; };
+        XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override { return *this; };
+        torch::Tensor predict(torch::Tensor& X) override { return torch::zeros(0); };
+        std::vector<int> predict(std::vector<std::vector<int>>& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override { return torch::zeros(0); };
+
         int getNumberOfNodes() const override { return 0; };
         int getNumberOfEdges() const override { return 0; };
         int getNumberOfStates() const override { return 0; };
         int getClassNumStates() const override { return 0; };
-        torch::Tensor predict(torch::Tensor& X) override { return torch::zeros(0); };
-        std::vector<int> predict(std::vector<std::vector<int>>& X) override;
-        torch::Tensor predict_proba(torch::Tensor& X) override { return torch::zeros(0); };
-        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
         bayesnet::status_t getStatus() const override { return status; }
-        std::string getVersion() override { return { project_version.begin(), project_version.end() }; };
+        std::string getVersion() override { return version; };
         float score(torch::Tensor& X, torch::Tensor& y) override { return 0; };
-        float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
         std::vector<std::string> show() const override { return {}; }
         std::vector<std::string> topological_order()  override { return {}; }
         std::vector<std::string> getNotes() const override { return notes; }
         std::string dump_cpt() const override { return ""; }
         void setHyperparameters(const nlohmann::json& hyperparameters) override;
-
         std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
+        void setDebug(bool debug) { this->debug = debug; }
     protected:
-        void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
+        void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override {};
 
     private:
         inline void normalize_weights(int num_instances)
@@ -61,7 +61,6 @@ namespace platform {
                 w = w * num_instances / sum;
             }
         }
-        // The instances of the dataset
         Xaode aode_;
         std::vector<double> weights_;
         CountingSemaphore& semaphore_;
@@ -69,6 +68,7 @@ namespace platform {
         bayesnet::status_t status = bayesnet::NORMAL;
         std::vector<std::string> notes;
         bool use_threads = false;
+        std::string version = "0.9.7";
     };
 }
 #endif // XA1DE_H
\ No newline at end of file