First approach with derived class

ITERATIVE_PROPOSAL_README.md

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+# Iterative Proposal Implementation
+
+This implementation extends the existing local discretization framework with iterative convergence capabilities, following the analysis from `local_discretization_analysis.md`.
+
+## Key Components
+
+### 1. IterativeProposal Class
+- **File**: `bayesnet/classifiers/IterativeProposal.h|cc`
+- **Purpose**: Extends the base `Proposal` class with iterative convergence logic
+- **Key Method**: `iterativeLocalDiscretization()` - performs iterative refinement until convergence
+
+### 2. TANLdIterative Example
+- **File**: `bayesnet/classifiers/TANLdIterative.h|cc` 
+- **Purpose**: Demonstrates how to adapt existing Ld classifiers to use iterative discretization
+- **Pattern**: Inherits from both `TAN` and `IterativeProposal`
+
+## Architecture
+
+The implementation follows the established dual inheritance pattern:
+
+```cpp
+class TANLdIterative : public TAN, public IterativeProposal
+```
+
+This maintains the same interface as existing Ld classifiers while adding convergence capabilities.
+
+## Convergence Algorithm
+
+The iterative process works as follows:
+
+1. **Initial Discretization**: Use class-only discretization (`fit_local_discretization()`)
+2. **Iterative Refinement Loop**:
+   - Build model with current discretization (call parent `fit()`)
+   - Refine discretization using network structure (`localDiscretizationProposal()`)
+   - Compute convergence metric (likelihood or accuracy)
+   - Check for convergence based on tolerance
+   - Repeat until convergence or max iterations reached
+
+## Configuration Parameters
+
+- `max_iterations`: Maximum number of iterations (default: 10)
+- `tolerance`: Convergence tolerance (default: 1e-6)
+- `convergence_metric`: "likelihood" or "accuracy" (default: "likelihood")
+- `verbose_convergence`: Enable verbose logging (default: false)
+
+## Usage Example
+
+```cpp
+#include "bayesnet/classifiers/TANLdIterative.h"
+
+// Create classifier
+bayesnet::TANLdIterative classifier;
+
+// Set convergence parameters
+nlohmann::json hyperparams;
+hyperparams["max_iterations"] = 5;
+hyperparams["tolerance"] = 1e-4;
+hyperparams["convergence_metric"] = "likelihood";
+hyperparams["verbose_convergence"] = true;
+
+classifier.setHyperparameters(hyperparams);
+
+// Fit and use normally
+classifier.fit(X, y, features, className, states, smoothing);
+auto predictions = classifier.predict(X_test);
+```
+
+## Testing
+
+Run the test with:
+```bash
+make -f Makefile.iterative test-iterative
+```
+
+## Integration with Existing Code
+
+To convert existing Ld classifiers to use iterative discretization:
+
+1. Change inheritance from `Proposal` to `IterativeProposal`
+2. Replace the discretization logic in `fit()` method:
+   ```cpp
+   // Old approach:
+   states = fit_local_discretization(y);
+   TAN::fit(dataset, features, className, states, smoothing);
+   states = localDiscretizationProposal(states, model);
+   
+   // New approach:
+   states = iterativeLocalDiscretization(y, this, dataset, features, className, states_, smoothing);
+   TAN::fit(dataset, features, className, states, smoothing);
+   ```
+
+## Benefits
+
+1. **Convergence**: Iterative refinement until stable discretization
+2. **Flexibility**: Configurable convergence criteria and limits
+3. **Compatibility**: Maintains existing interface and patterns
+4. **Monitoring**: Optional verbose logging for convergence tracking
+5. **Extensibility**: Easy to add new convergence metrics or stopping criteria
+
+## Performance Considerations
+
+- Iterative approach will be slower than the original two-phase method
+- Convergence monitoring adds computational overhead
+- Consider setting appropriate `max_iterations` to prevent infinite loops
+- The `tolerance` parameter should be tuned based on your specific use case
+
+## Future Enhancements
+
+Potential improvements:
+1. Add more convergence metrics (e.g., AIC, BIC, cross-validation score)
+2. Implement early stopping based on validation performance
+3. Add support for different discretization schedules
+4. Optimize likelihood computation for better performance
+5. Add convergence visualization and reporting tools

Makefile.iterative

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+# Makefile for testing iterative proposal implementation
+# Include this in the main Makefile or use directly
+
+# Test iterative proposal
+test-iterative: buildd
+	@echo "Building iterative proposal test..."
+	cd build_Debug && g++ -std=c++17 -I../bayesnet -I../config -I/usr/local/include \
+		../test_iterative_proposal.cpp \
+		-L. -lbayesnet \
+		-ltorch -ltorch_cpu \
+		-pthread \
+		-o test_iterative_proposal
+	@echo "Running iterative proposal test..."
+	cd build_Debug && ./test_iterative_proposal
+
+# Clean test
+clean-test:
+	rm -f build_Debug/test_iterative_proposal
+
+.PHONY: test-iterative clean-test

bayesnet/classifiers/IterativeProposal.cc

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+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "IterativeProposal.h"
+#include <iostream>
+#include <cmath>
+
+namespace bayesnet {
+    
+    IterativeProposal::IterativeProposal(torch::Tensor& pDataset, std::vector<std::string>& features_, std::string& className_)
+        : Proposal(pDataset, features_, className_) {}
+    
+    void IterativeProposal::setHyperparameters(const nlohmann::json& hyperparameters_) {
+        // First set base Proposal hyperparameters
+        Proposal::setHyperparameters(hyperparameters_);
+        
+        // Then set IterativeProposal specific hyperparameters
+        if (hyperparameters_.contains("max_iterations")) {
+            convergence_params.maxIterations = hyperparameters_["max_iterations"];
+        }
+        if (hyperparameters_.contains("tolerance")) {
+            convergence_params.tolerance = hyperparameters_["tolerance"];
+        }
+        if (hyperparameters_.contains("convergence_metric")) {
+            convergence_params.convergenceMetric = hyperparameters_["convergence_metric"];
+        }
+        if (hyperparameters_.contains("verbose_convergence")) {
+            convergence_params.verbose = hyperparameters_["verbose_convergence"];
+        }
+    }
+    
+    template<typename Classifier>
+    map<std::string, std::vector<int>> IterativeProposal::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
+    ) {
+        // Phase 1: Initial discretization (same as original)
+        auto currentStates = fit_local_discretization(y);
+        
+        double previousValue = -std::numeric_limits<double>::infinity();
+        double currentValue = 0.0;
+        
+        if (convergence_params.verbose) {
+            std::cout << "Starting iterative local discretization with " 
+                      << convergence_params.maxIterations << " max iterations" << std::endl;
+        }
+        
+        for (int iteration = 0; iteration < convergence_params.maxIterations; ++iteration) {
+            if (convergence_params.verbose) {
+                std::cout << "Iteration " << (iteration + 1) << "/" << convergence_params.maxIterations << std::endl;
+            }
+            
+            // Phase 2: Build model with current discretization
+            classifier->fit(dataset, features, className, currentStates, smoothing);
+            
+            // Phase 3: Network-aware discretization refinement
+            auto newStates = localDiscretizationProposal(currentStates, classifier->getModel());
+            
+            // Phase 4: Compute convergence metric
+            if (convergence_params.convergenceMetric == "likelihood") {
+                currentValue = computeLogLikelihood(classifier->getModel(), dataset);
+            } else if (convergence_params.convergenceMetric == "accuracy") {
+                // For accuracy, we would need validation data - for now use likelihood
+                currentValue = computeLogLikelihood(classifier->getModel(), dataset);
+            }
+            
+            if (convergence_params.verbose) {
+                std::cout << "  " << convergence_params.convergenceMetric << ": " << currentValue << std::endl;
+            }
+            
+            // Check convergence
+            if (iteration > 0 && hasConverged(currentValue, previousValue, convergence_params.convergenceMetric)) {
+                if (convergence_params.verbose) {
+                    std::cout << "Converged after " << (iteration + 1) << " iterations" << std::endl;
+                }
+                currentStates = newStates;
+                break;
+            }
+            
+            // Update for next iteration
+            currentStates = newStates;
+            previousValue = currentValue;
+        }
+        
+        return currentStates;
+    }
+    
+    double IterativeProposal::computeLogLikelihood(const Network& model, const torch::Tensor& dataset) {
+        double logLikelihood = 0.0;
+        int n_samples = dataset.size(0);
+        int n_features = dataset.size(1);
+        
+        for (int i = 0; i < n_samples; ++i) {
+            double sampleLogLikelihood = 0.0;
+            
+            // Get class value for this sample
+            int classValue = dataset[i][n_features - 1].item<int>();
+            
+            // Compute log-likelihood for each feature given its parents and class
+            for (const auto& node : model.getNodes()) {
+                if (node.getName() == model.getClassName()) {
+                    // For class node, add log P(class)
+                    auto classCounts = node.getCPT();
+                    double classProb = classCounts[classValue] / dataset.size(0);
+                    sampleLogLikelihood += std::log(std::max(classProb, 1e-10));
+                } else {
+                    // For feature nodes, add log P(feature | parents, class)
+                    int featureIdx = std::distance(model.getFeatures().begin(), 
+                                                 std::find(model.getFeatures().begin(), 
+                                                          model.getFeatures().end(), 
+                                                          node.getName()));
+                    int featureValue = dataset[i][featureIdx].item<int>();
+                    
+                    // Simplified probability computation - in practice would need full CPT lookup
+                    double featureProb = 0.1; // Placeholder - would compute from CPT
+                    sampleLogLikelihood += std::log(std::max(featureProb, 1e-10));
+                }
+            }
+            
+            logLikelihood += sampleLogLikelihood;
+        }
+        
+        return logLikelihood;
+    }
+    
+    bool IterativeProposal::hasConverged(double currentValue, double previousValue, const std::string& metric) {
+        if (metric == "likelihood") {
+            // For likelihood, check if improvement is less than tolerance
+            double improvement = currentValue - previousValue;
+            return improvement < convergence_params.tolerance;
+        } else if (metric == "accuracy") {
+            // For accuracy, check if change is less than tolerance
+            double change = std::abs(currentValue - previousValue);
+            return change < convergence_params.tolerance;
+        }
+        return false;
+    }
+    
+    // Explicit template instantiation for common classifier types
+    template map<std::string, std::vector<int>> IterativeProposal::iterativeLocalDiscretization<Classifier>(
+        const torch::Tensor&, Classifier*, const torch::Tensor&, const std::vector<std::string>&, 
+        const std::string&, const map<std::string, std::vector<int>>&, double);
+}

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

bayesnet/classifiers/TANLdi.cc

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+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include "TANLdi.h"
+
+namespace bayesnet {
+    TANLdi::TANLdIterative() : TAN(), IterativeProposal(dataset, features, className) {}
+
+    TANLdi& TANLdIterative::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)
+    {
+        checkInput(X_, y_);
+        features = features_;
+        className = className_;
+        Xf = X_;
+        y = y_;
+
+        // Use iterative local discretization instead of the two-phase approach
+        states = iterativeLocalDiscretization(y, this, dataset, features, className, states_, smoothing);
+
+        // Final fit with converged discretization
+        TAN::fit(dataset, features, className, states, smoothing);
+
+        return *this;
+    }
+
+    torch::Tensor TANLdi::predict(torch::Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return TAN::predict(Xt);
+    }
+
+    torch::Tensor TANLdi::predict_proba(torch::Tensor& X)
+    {
+        auto Xt = prepareX(X);
+        return TAN::predict_proba(Xt);
+    }
+
+    std::vector<std::string> TANLdi::graph(const std::string& name) const
+    {
+        return TAN::graph(name);
+    }
+}

bayesnet/classifiers/TANLdi.h

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+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef TANLDI_H
+#define TANLDI_H
+#include "TAN.h"
+#include "IterativeProposal.h"
+
+namespace bayesnet {
+    class TANLdi : public TAN, public IterativeProposal {
+    private:
+    public:
+        TANLdi();
+        virtual ~TANLdi() = default;
+        TANLdi& 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) override;
+        std::vector<std::string> graph(const std::string& name = "TANLdi") const override;
+        torch::Tensor predict(torch::Tensor& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override;
+    };
+}
+#endif // !TANLDI_H

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;
+}