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/classifiers/Proposal.h

@@ -10,14 +10,16 @@
 #include <map>
 #include <torch/torch.h>
 #include <fimdlp/CPPFImdlp.h>
+#include <fimdlp/BinDisc.h>
 #include "bayesnet/network/Network.h"
+#include <nlohmann/json.hpp>
 #include "Classifier.h"
 
 namespace bayesnet {
     class Proposal {
     public:
         Proposal(torch::Tensor& pDataset, std::vector<std::string>& features_, std::string& className_);
-        virtual ~Proposal();
+        void setHyperparameters(const nlohmann::json& hyperparameters_);
     protected:
         void checkInput(const torch::Tensor& X, const torch::Tensor& y);
         torch::Tensor prepareX(torch::Tensor& X);
@@ -25,12 +27,24 @@ namespace bayesnet {
         map<std::string, std::vector<int>> fit_local_discretization(const torch::Tensor& y);
         torch::Tensor Xf; // X continuous nxm tensor
         torch::Tensor y; // y discrete nx1 tensor
-        map<std::string, mdlp::CPPFImdlp*> discretizers;
+        map<std::string, std::unique_ptr<mdlp::Discretizer>> discretizers;
+        // MDLP parameters
+        struct {
+            size_t min_length = 3; // Minimum length of the interval to consider it in mdlp
+            float proposed_cuts = 0.0; // Proposed cuts for the Discretization algorithm
+            int max_depth = std::numeric_limits<int>::max(); // Maximum depth of the MDLP tree
+        } ld_params;
+        nlohmann::json validHyperparameters_ld = { "ld_algorithm", "ld_proposed_cuts", "mdlp_min_length", "mdlp_max_depth" };
     private:
         std::vector<int> factorize(const std::vector<std::string>& labels_t);
         torch::Tensor& pDataset; // (n+1)xm tensor
         std::vector<std::string>& pFeatures;
         std::string& pClassName;
+        enum class discretization_t {
+            MDLP,
+            BINQ,
+            BINU
+        } discretizationType = discretization_t::MDLP; // Default discretization type
     };
 }