src/BayesNet/BoostAODE.cc

@@ -11,12 +11,8 @@ namespace bayesnet {
     void BoostAODE::setHyperparameters(nlohmann::json& hyperparameters)
     {
         // Check if hyperparameters are valid
-        auto validKeys = { "repeatSparent", "maxModels", "ascending" };
-        for (const auto& item : hyperparameters.items()) {
-            if (find(validKeys.begin(), validKeys.end(), item.key()) == validKeys.end()) {
-                throw invalid_argument("Hyperparameter " + item.key() + " is not valid");
-            }
-        }
+        const vector<string> validKeys = { "repeatSparent", "maxModels", "ascending" };
+        checkHyperparameters(validKeys, hyperparameters);
         if (hyperparameters.contains("repeatSparent")) {
             repeatSparent = hyperparameters["repeatSparent"];
         }

src/BayesNet/Classifier.cc

@@ -152,4 +152,12 @@ namespace bayesnet {
     {
         model.dump_cpt();
     }
+    void Classifier::checkHyperparameters(const vector<string>& validKeys, nlohmann::json& hyperparameters)
+    {
+        for (const auto& item : hyperparameters.items()) {
+            if (find(validKeys.begin(), validKeys.end(), item.key()) == validKeys.end()) {
+                throw invalid_argument("Hyperparameter " + item.key() + " is not valid");
+            }
+        }
+    }
 }

src/BayesNet/Classifier.h

@@ -24,6 +24,7 @@ namespace bayesnet {
         void checkFitParameters();
         virtual void buildModel(const torch::Tensor& weights) = 0;
         void trainModel(const torch::Tensor& weights) override;
+        void checkHyperparameters(const vector<string>& validKeys, nlohmann::json& hyperparameters);
     public:
         Classifier(Network model);
         virtual ~Classifier() = default;