Begin model inclusion

2025-02-18 10:48:46 +01:00
parent 17728212c1
commit bd5ba14f04
14 changed files with 967 additions and 71 deletions
--- a/src/experimental_clfs/XA1DE.h
+++ b/src/experimental_clfs/XA1DE.h
@@ -0,0 +1,74 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#ifndef XA1DE_H
+#define XA1DE_H
+#include <iostream>
+#include <vector>
+#include <cmath>
+#include <algorithm>
+#include <limits>
+#include "bayesnet/BaseClassifier.h"
+#include "common/Timer.hpp"
+#include "CountingSemaphore.hpp"
+#include "Xaode.hpp"
+
+namespace platform {
+    class XA1DE : public bayesnet::BaseClassifier {
+    public:
+        XA1DE();
+        virtual ~XA1DE() = default;
+        void setDebug(bool debug) { this->debug = debug; }
+        std::vector<std::vector<double>> predict_proba_threads(const std::vector<std::vector<int>>& test_data);
+
+        XA1DE& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
+        XA1DE& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
+        XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
+        XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
+        int getNumberOfNodes() const override { return 0; };
+        int getNumberOfEdges() const override { return 0; };
+        int getNumberOfStates() const override { return 0; };
+        int getClassNumStates() const override { return 0; };
+        torch::Tensor predict(torch::Tensor& X) override { return torch::zeros(0); };
+        std::vector<int> predict(std::vector<std::vector<int>>& X) override;
+        torch::Tensor predict_proba(torch::Tensor& X) override { return torch::zeros(0); };
+        std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
+        bayesnet::status_t getStatus() const override { return status; }
+        std::string getVersion() override { return { project_version.begin(), project_version.end() }; };
+        float score(torch::Tensor& X, torch::Tensor& y) override { return 0; };
+        float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
+        std::vector<std::string> show() const override { return {}; }
+        std::vector<std::string> topological_order()  override { return {}; }
+        std::vector<std::string> getNotes() const override { return notes; }
+        std::string dump_cpt() const override { return ""; }
+        void setHyperparameters(const nlohmann::json& hyperparameters) override;
+
+        std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
+    protected:
+        void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
+
+    private:
+        inline void normalize_weights(int num_instances)
+        {
+            double sum = std::accumulate(weights_.begin(), weights_.end(), 0.0);
+            if (sum == 0) {
+                throw std::runtime_error("Weights sum zero.");
+            }
+            for (double& w : weights_) {
+                w = w * num_instances / sum;
+            }
+        }
+        // The instances of the dataset
+        Xaode aode_;
+        std::vector<double> weights_;
+        CountingSemaphore& semaphore_;
+        bool debug = false;
+        bayesnet::status_t status = bayesnet::NORMAL;
+        std::vector<std::string> notes;
+        bool use_threads = false;
+    };
+}
+#endif // XA1DE_H