First implemented aproximation

bayesnet/ensembles/WA2DE.h

@@ -0,0 +1,53 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+#ifndef WA2DE_H
+#define WA2DE_H
+#include "Ensemble.h"
+#include <torch/torch.h>
+#include <vector>
+#include <map>
+#include <nlohmann/json.hpp>
+namespace bayesnet {
+    /**
+     * Geoffrey I. Webb's A2DE (Averaged 2-Dependence Estimators) classifier
+     * Implements the A2DE algorithm as an ensemble of SPODE models.
+     */
+    class WA2DE : public Ensemble {
+    public:
+        explicit WA2DE(bool predict_voting = false);
+        virtual ~WA2DE() {};
+
+        // Override method to set hyperparameters
+        void setHyperparameters(const nlohmann::json& hyperparameters) override;
+
+        // Graph visualization function
+        std::vector<std::string> graph(const std::string& title = "A2DE") const override;
+        torch::Tensor computeProbabilities(const torch::Tensor& data) const;
+        double score(const torch::Tensor& X, const torch::Tensor& y);
+    protected:
+        // Model-building function
+        void buildModel(const torch::Tensor& weights) override;
+
+    private:
+        int num_classes_;                // Number of classes
+        int num_attributes_;             // Number of attributes
+        std::vector<int> attribute_cardinalities_; // Cardinalities of attributes
+
+        // Frequency counts (similar to Java implementation)
+        std::vector<double> class_counts_;  // Class frequency
+        std::vector<std::vector<std::vector<double>>> freq_attr_class_; // P(A | C)
+        std::vector<std::vector<std::vector<std::vector<std::vector<double>>>>> freq_pair_class_; // P(A_i, A_j | C)
+
+        double total_count_; // Total instance count
+
+        bool weighted_a2de_; // Whether to use weighted A2DE
+        double smoothing_factor_; // Smoothing parameter (default: Laplace)
+        torch::Tensor AODEConditionalProb(const torch::Tensor& data);
+        void trainModel(const torch::Tensor& data, const Smoothing_t smoothing);
+        int toIntValue(int attributeIndex, float value) const;
+    };
+}
+#endif