Complete TAN with Maximum Spanning Tree

src/Metrics.cc

@@ -1,4 +1,5 @@
 #include "Metrics.hpp"
+#include "Mst.h"
 using namespace std;
 namespace bayesnet {
     Metrics::Metrics(torch::Tensor& samples, vector<string>& features, string& className, int classNumStates)
@@ -121,14 +122,11 @@ namespace bayesnet {
     and the indices of the weights as nodes of this square matrix using
     Kruskal algorithm
     */
-    vector<pair<int, int>> Metrics::maximumSpanningTree(int root, Tensor& weights)
+    vector<pair<int, int>> Metrics::maximumSpanningTree(vector<string> features, Tensor& weights, int root)
     {
         auto result = vector<pair<int, int>>();
+        auto mst = MST(features, weights, root);
+        return mst.maximumSpanningTree();
 
-
-
-
-
-        return result;
     }
 }