Fix XSPode

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();
+    }
 }
 

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;

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;
     };
 }