Implement 3 types of smoothing

bayesnet/BaseClassifier.h

@@ -8,10 +8,13 @@
 #include <vector>
 #include <torch/torch.h>
 #include <nlohmann/json.hpp>
+#include "bayesnet/network/Network.h"
+
 namespace bayesnet {
     enum status_t { NORMAL, WARNING, ERROR };
     class BaseClassifier {
     public:
+        void setSmoothing(Smoothing_t smoothing) { this->smoothing = smoothing; } // To call before fit
         // X is nxm std::vector, y is nx1 std::vector
         virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
         // X is nxm tensor, y is nx1 tensor
@@ -41,5 +44,6 @@ namespace bayesnet {
     protected:
         virtual void trainModel(const torch::Tensor& weights) = 0;
         std::vector<std::string> validHyperparameters;
+        Smoothing_t smoothing = Smoothing_t::NONE;
     };
 }