Fix XSpode

bayesnet/classifiers/XSPODE.cc

@@ -45,7 +45,7 @@ namespace bayesnet {
         n = X.size();
         buildModel(weights_);
         trainModel(weights_, smoothing);
-        fitted=true;
+        fitted = true;
     }
 
     // --------------------------------------
@@ -398,21 +398,14 @@ namespace bayesnet {
         return result;
     }
     torch::Tensor XSpode::predict_proba(torch::Tensor& X)
-    {
-       auto X_ = TensorUtils::to_matrix(X);
-       auto result_v = predict_proba(X_);
-       torch::Tensor result;
-       for (int i = 0; i < result_v.size(); ++i) {
-           result.index_put_({ i, "..." }, torch::tensor(result_v[i], torch::kDouble));
-       }
-       return result;
-    }
-    torch::Tensor XSpode::predict(torch::Tensor& X)
     {
         auto X_ = TensorUtils::to_matrix(X);
-        auto predict = predict(X_);
-        return TensorUtils::to_tensor(predict);
+        auto result_v = predict_proba(X_);
+        torch::Tensor result;
+        for (int i = 0; i < result_v.size(); ++i) {
+            result.index_put_({ i, "..." }, torch::tensor(result_v[i], torch::kDouble));
+        }
+        return result;
     }
-
 }
 

bayesnet/classifiers/XSPODE.h

@@ -28,7 +28,7 @@ namespace bayesnet {
         int getNumberOfStates() const override;
         int getClassNumStates() const override;
         std::vector<int>& getStates();
-        std::vector<std::string> graph(const std::string& title) const override { return std::vector<std::string>({title}); }
+        std::vector<std::string> graph(const std::string& title) const override { return std::vector<std::string>({ title }); }
         void fit(std::vector<std::vector<int>>& X, std::vector<int>& y, torch::Tensor& weights_, const Smoothing_t smoothing);
         void setHyperparameters(const nlohmann::json& hyperparameters_) override;
 
@@ -38,7 +38,6 @@ 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;
-        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
     protected:
         void buildModel(const torch::Tensor& weights) override;
         void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;