diff --git a/bayesnet/classifiers/XSPODE.cc b/bayesnet/classifiers/XSPODE.cc
index ff7d332..ab0bf69 100644
--- a/bayesnet/classifiers/XSPODE.cc
+++ b/bayesnet/classifiers/XSPODE.cc
@@ -407,5 +407,24 @@ namespace bayesnet {
         }
         return result;
     }
+    float XSpode::score(torch::Tensor& X, torch::Tensor& y)
+    {
+        torch::Tensor y_pred = predict(X);
+        return (y_pred == y).sum().item<float>() / y.size(0);
+    }
+    float XSpode::score(std::vector<std::vector<int>>& X, std::vector<int>& y)
+    {
+        if (!fitted) {
+            throw std::logic_error(CLASSIFIER_NOT_FITTED);
+        }
+        auto y_pred = this->predict(X);
+        int correct = 0;
+        for (int i = 0; i < y_pred.size(); ++i) {
+            if (y_pred[i] == y[i]) {
+                correct++;
+            }
+        }
+        return (double)correct / y_pred.size();
+    }
 }
 
diff --git a/bayesnet/classifiers/XSPODE.h b/bayesnet/classifiers/XSPODE.h
index 215e8ab..a8c24af 100644
--- a/bayesnet/classifiers/XSPODE.h
+++ b/bayesnet/classifiers/XSPODE.h
@@ -38,6 +38,8 @@ namespace bayesnet {
         torch::Tensor predict(torch::Tensor& X) override;
         std::vector<int> predict(std::vector<std::vector<int>>& X) override;
         torch::Tensor predict_proba(torch::Tensor& X) override;
+        float score(torch::Tensor& X, torch::Tensor& y) override;
+        float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
     protected:
         void buildModel(const torch::Tensor& weights) override;
         void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
diff --git a/bayesnet/ensembles/WA2DE.h b/bayesnet/ensembles/WA2DE.h
index 7008025..246ce7b 100644
--- a/bayesnet/ensembles/WA2DE.h
+++ b/bayesnet/ensembles/WA2DE.h
@@ -30,7 +30,7 @@ namespace bayesnet {
     protected:
         // Model-building function
         void buildModel(const torch::Tensor& weights) override;
-
+        void trainModel(const torch::Tensor& data, const Smoothing_t smoothing) override;
     private:
         int num_classes_;                // Number of classes
         int num_attributes_;             // Number of attributes
@@ -46,7 +46,6 @@ namespace bayesnet {
         bool weighted_a2de_; // Whether to use weighted A2DE
         double smoothing_factor_; // Smoothing parameter (default: Laplace)
         torch::Tensor AODEConditionalProb(const torch::Tensor& data);
-        void trainModel(const torch::Tensor& data, const Smoothing_t smoothing);
         int toIntValue(int attributeIndex, float value) const;
     };
 }
diff --git a/sample/CMakeLists.txt b/sample/CMakeLists.txt
index fbcfdcc..d3e79a4 100644
--- a/sample/CMakeLists.txt
+++ b/sample/CMakeLists.txt
@@ -22,4 +22,4 @@ include_directories(
 )
 
 add_executable(bayesnet_sample sample.cc) 
-target_link_libraries(bayesnet_sample fimdlp "${TORCH_LIBRARIES}" "${BayesNet}")
\ No newline at end of file
+target_link_libraries(bayesnet_sample ${FImdlp} "${TORCH_LIBRARIES}" "${BayesNet}")
\ No newline at end of file