Add new hyperparameters to the Ld classifiers

- *ld_algorithm*: algorithm to use for local discretization, with the following options: "MDLP", "BINQ", "BINU".
  - *ld_proposed_cuts*: number of cut points to return.
  - *mdlp_min_length*: minimum length of a partition in MDLP algorithm to be evaluated for partition.
  - *mdlp_max_depth*: maximum level of recursion in MDLP algorithm.

bayesnet/ensembles/AODELd.cc

@@ -9,6 +9,7 @@
 namespace bayesnet {
     AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
     {
+        validHyperparameters = validHyperparameters_ld; // Inherits the valid hyperparameters from Proposal
     }
     AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_, const Smoothing_t smoothing)
     {
@@ -31,6 +32,7 @@ namespace bayesnet {
         models.clear();
         for (int i = 0; i < features.size(); ++i) {
             models.push_back(std::make_unique<SPODELd>(i));
+            models.back()->setHyperparameters(hyperparameters);
         }
         n_models = models.size();
         significanceModels = std::vector<double>(n_models, 1.0);

bayesnet/ensembles/AODELd.h

@@ -20,6 +20,8 @@ namespace bayesnet {
     protected:
         void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void buildModel(const torch::Tensor& weights) override;
+    private:
+        nlohmann::json hyperparameters = {}; // Hyperparameters for the model
     };
 }
 #endif // !AODELD_H