First approach with derived class

bayesnet/classifiers/IterativeProposal.h

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+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef ITERATIVE_PROPOSAL_H
+#define ITERATIVE_PROPOSAL_H
+
+#include "Proposal.h"
+#include "bayesnet/network/Network.h"
+#include <nlohmann/json.hpp>
+
+namespace bayesnet {
+    class IterativeProposal : public Proposal {
+    public:
+        IterativeProposal(torch::Tensor& pDataset, std::vector<std::string>& features_, std::string& className_);
+        void setHyperparameters(const nlohmann::json& hyperparameters_);
+        
+    protected:
+        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,
+            double smoothing = 1.0
+        );
+        
+        // Convergence parameters
+        struct {
+            int maxIterations = 10;
+            double tolerance = 1e-6;
+            std::string convergenceMetric = "likelihood"; // "likelihood" or "accuracy"
+            bool verbose = false;
+        } convergence_params;
+        
+        nlohmann::json validHyperparameters_iter = { 
+            "max_iterations", "tolerance", "convergence_metric", "verbose_convergence" 
+        };
+        
+    private:
+        double computeLogLikelihood(const Network& model, const torch::Tensor& dataset);
+        bool hasConverged(double currentValue, double previousValue, const std::string& metric);
+    };
+}
+
+#endif