Refactor Library 2 include a Platform/ Experiments

src/TAN.cc

@@ -1,42 +0,0 @@
-#include "TAN.h"
-
-namespace bayesnet {
-    using namespace std;
-    using namespace torch;
-
-    TAN::TAN() : BaseClassifier(Network()) {}
-
-    void TAN::train()
-    {
-        // 0. Add all nodes to the model
-        addNodes();
-        // 1. Compute mutual information between each feature and the class and set the root node
-        // as the highest mutual information with the class
-        auto mi = vector <pair<int, float >>();
-        Tensor class_dataset = dataset.index({ "...", -1 });
-        for (int i = 0; i < static_cast<int>(features.size()); ++i) {
-            Tensor feature_dataset = dataset.index({ "...", i });
-            auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset);
-            mi.push_back({ i, mi_value });
-        }
-        sort(mi.begin(), mi.end(), [](auto& left, auto& right) {return left.second < right.second;});
-        auto root = mi[mi.size() - 1].first;
-        // 2. Compute mutual information between each feature and the class
-        auto weights = metrics.conditionalEdge();
-        // 3. Compute the maximum spanning tree
-        auto mst = metrics.maximumSpanningTree(features, weights, root);
-        // 4. Add edges from the maximum spanning tree to the model
-        for (auto i = 0; i < mst.size(); ++i) {
-            auto [from, to] = mst[i];
-            model.addEdge(features[from], features[to]);
-        }
-        // 5. Add edges from the class to all features
-        for (auto feature : features) {
-            model.addEdge(className, feature);
-        }
-    }
-    vector<string> TAN::graph(string title)
-    {
-        return model.graph(title);
-    }
-}