Complete proposal

bayesnet/classifiers/Proposal.h

@@ -25,18 +25,43 @@ namespace bayesnet {
         torch::Tensor prepareX(torch::Tensor& X);
         map<std::string, std::vector<int>> localDiscretizationProposal(const map<std::string, std::vector<int>>& states, Network& model);
         map<std::string, std::vector<int>> fit_local_discretization(const torch::Tensor& y);
+
+        // Iterative discretization method
+        template<typename Classifier>
+        map<std::string, std::vector<int>> iterativeLocalDiscretization(
+            const torch::Tensor& y,
+            Classifier* classifier,
+            const torch::Tensor& dataset,
+            const std::vector<std::string>& features,
+            const std::string& className,
+            const map<std::string, std::vector<int>>& initialStates,
+            const Smoothing_t smoothing
+        );
+
         torch::Tensor Xf; // X continuous nxm tensor
         torch::Tensor y; // y discrete nx1 tensor
         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" };
+
+        // Convergence parameters
+        struct {
+            int maxIterations = 10;
+            bool verbose = false;
+        } convergence_params;
+
+        nlohmann::json validHyperparameters_ld = {
+            "ld_algorithm", "ld_proposed_cuts", "mdlp_min_length", "mdlp_max_depth",
+            "max_iterations", "verbose_convergence"
+        };
     private:
         std::vector<int> factorize(const std::vector<std::string>& labels_t);
+        double computeLogLikelihood(Network& model, const torch::Tensor& dataset);
         torch::Tensor& pDataset; // (n+1)xm tensor
         std::vector<std::string>& pFeatures;
         std::string& pClassName;