Begin implementing predict_single hyperparameter in BoostAODE

src/BoostAODE.h

@@ -15,17 +15,21 @@ namespace bayesnet {
         void buildModel(const torch::Tensor& weights) override;
         void trainModel(const torch::Tensor& weights) override;
     private:
+        std::unordered_set<int> initializeModels();
+        torch::Tensor ensemble_predict(torch::Tensor& X, SPODE* model);
         torch::Tensor dataset_;
         torch::Tensor X_train, y_train, X_test, y_test;
-        std::unordered_set<int> initializeModels();
         // Hyperparameters
         bool repeatSparent = false; // if true, a feature can be selected more than once
         int maxModels = 0;
         int tolerance = 0;
+        bool predict_single = true; // wether the last model is used to predict in training or the whole ensemble
         std::string order_algorithm; // order to process the KBest features asc, desc, rand
         bool convergence = false; //if true, stop when the model does not improve
         bool selectFeatures = false; // if true, use feature selection
-        std::string select_features_algorithm = ""; // Selected feature selection algorithm
+        std::string select_features_algorithm = "desc"; // Selected feature selection algorithm
+        bool initialize_prob_table; // if true, initialize the prob_table with the first model (used in train)
+        torch::Tensor prob_table; // Table of probabilities for ensemble predicting if predict_single is false
         FeatureSelect* featureSelector = nullptr;
         double threshold = -1;
     };