Fix XSpode predict

bayesnet/classifiers/XSPODE.h

@@ -8,15 +8,6 @@
 #define XSPODE_H
 
 #include <vector>
-#include <map>
-#include <stdexcept>
-#include <algorithm>
-#include <numeric>
-#include <string>
-#include <cmath>
-#include <limits>
-#include <sstream>
-#include <iostream>
 #include <torch/torch.h>
 #include  "Classifier.h"
 #include "bayesnet/utils/CountingSemaphore.h"
@@ -32,7 +23,6 @@ namespace bayesnet {
         std::vector<int> predict(std::vector<std::vector<int>>& test_data);
         void normalize(std::vector<double>& v) const;
         std::string to_string() const;
-        int statesClass() const;
         int getNFeatures() const;
         int getNumberOfNodes() const override;
         int getNumberOfEdges() const override;
@@ -41,6 +31,15 @@ namespace bayesnet {
         std::vector<int>& getStates();
         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;
+
+        //
+        // Classifier interface
+        //
+        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;