Refactor library structure

bayesnet/ensembles/AODELd.cc

@@ -0,0 +1,54 @@
+#include "AODELd.h"
+
+namespace bayesnet {
+    AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
+    {
+        validHyperparameters = { "predict_voting" };
+
+    }
+    void AODELd::setHyperparameters(const nlohmann::json& hyperparameters_)
+    {
+        auto hyperparameters = hyperparameters_;
+        if (hyperparameters.contains("predict_voting")) {
+            predict_voting = hyperparameters["predict_voting"];
+            hyperparameters.erase("predict_voting");
+        }
+        if (!hyperparameters.empty()) {
+            throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
+        }
+    }
+    AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
+    {
+        checkInput(X_, y_);
+        features = features_;
+        className = className_;
+        Xf = X_;
+        y = y_;
+        // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
+        states = fit_local_discretization(y);
+        // We have discretized the input data
+        // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
+        Ensemble::fit(dataset, features, className, states);
+        return *this;
+
+    }
+    void AODELd::buildModel(const torch::Tensor& weights)
+    {
+        models.clear();
+        for (int i = 0; i < features.size(); ++i) {
+            models.push_back(std::make_unique<SPODELd>(i));
+        }
+        n_models = models.size();
+        significanceModels = std::vector<double>(n_models, 1.0);
+    }
+    void AODELd::trainModel(const torch::Tensor& weights)
+    {
+        for (const auto& model : models) {
+            model->fit(Xf, y, features, className, states);
+        }
+    }
+    std::vector<std::string> AODELd::graph(const std::string& name) const
+    {
+        return Ensemble::graph(name);
+    }
+}