First approach with derived class

test_iterative_proposal.cpp

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+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <iostream>
+#include <torch/torch.h>
+#include <nlohmann/json.hpp>
+#include "bayesnet/classifiers/TANLdIterative.h"
+
+using json = nlohmann::json;
+
+int main() {
+    std::cout << "Testing Iterative Proposal Implementation" << std::endl;
+    
+    // Create synthetic continuous data
+    torch::Tensor X = torch::rand({100, 3}); // 100 samples, 3 features
+    torch::Tensor y = torch::randint(0, 2, {100}); // Binary classification
+    
+    // Create feature names
+    std::vector<std::string> features = {"feature1", "feature2", "feature3"};
+    std::string className = "class";
+    
+    // Create initial states (will be updated by discretization)
+    std::map<std::string, std::vector<int>> states;
+    states[className] = {0, 1};
+    
+    // Create classifier
+    bayesnet::TANLdIterative classifier;
+    
+    // Set convergence hyperparameters
+    json hyperparams;
+    hyperparams["max_iterations"] = 5;
+    hyperparams["tolerance"] = 1e-4;
+    hyperparams["convergence_metric"] = "likelihood";
+    hyperparams["verbose_convergence"] = true;
+    
+    classifier.setHyperparameters(hyperparams);
+    
+    try {
+        // Fit the model
+        std::cout << "Fitting TANLdIterative classifier..." << std::endl;
+        classifier.fit(X, y, features, className, states, bayesnet::Smoothing_t::LAPLACE);
+        
+        // Make predictions
+        torch::Tensor X_test = torch::rand({10, 3});
+        torch::Tensor predictions = classifier.predict(X_test);
+        torch::Tensor probabilities = classifier.predict_proba(X_test);
+        
+        std::cout << "Predictions: " << predictions << std::endl;
+        std::cout << "Probabilities shape: " << probabilities.sizes() << std::endl;
+        
+        // Generate graph
+        auto graph = classifier.graph();
+        std::cout << "Graph nodes: " << graph.size() << std::endl;
+        
+        std::cout << "Test completed successfully!" << std::endl;
+        
+    } catch (const std::exception& e) {
+        std::cerr << "Error: " << e.what() << std::endl;
+        return 1;
+    }
+    
+    return 0;
+}