First approach with derived class

bayesnet/classifiers/TANLdi.cc

@@ -0,0 +1,45 @@
+// ***************************************************************
+// 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);
+    }
+}