refactor to use in python

sample/main.cc

@@ -232,6 +232,11 @@ int main(int argc, char** argv)
     unsigned int nthreads = std::thread::hardware_concurrency();
     cout << "Computer has " << nthreads << " cores." << endl;
     auto metrics = bayesnet::Metrics(network.getSamples(), features, className, network.getClassNumStates());
-    cout << "conditionalEdgeWeight " << endl << metrics.conditionalEdgeWeight() << endl;
+    cout << "conditionalEdgeWeight " << endl;
+    auto conditional = metrics.conditionalEdgeWeights();
+    cout << conditional << endl;
+    long m = features.size() + 1;
+    auto matrix = torch::from_blob(conditional.data(), { m, m });
+    cout << matrix << endl;
     return 0;
 }

sample/test.cc

@@ -167,6 +167,9 @@ int main()
     }
     // Print the resulting 3x3 tensor
     std::cout << tensor_3x3 << std::endl;
+    vector<int> v = { 1,2,3,4,5 };
+    torch::Tensor t = torch::tensor(v);
+    cout << t << endl;
 
 
 

src/Metrics.cc

@@ -1,12 +1,30 @@
 #include "Metrics.hpp"
 using namespace std;
 namespace bayesnet {
+    vector<int> linearize(const vector<vector<int>>& vec_vec)
+    {
+        vector<int> vec;
+        for (const auto& v : vec_vec) {
+            for (auto d : v) {
+                vec.push_back(d);
+            }
+        }
+        return vec;
+    }
     Metrics::Metrics(torch::Tensor& samples, vector<string>& features, string& className, int classNumStates)
         : samples(samples)
         , features(features)
         , className(className)
         , classNumStates(classNumStates)
     {
+
+    }
+    Metrics::Metrics(vector<vector<int>>& vsamples, int m, int n, vector<string>& features, string& className, int classNumStates)
+        : features(features)
+        , className(className)
+        , classNumStates(classNumStates)
+    {
+        samples = torch::from_blob(linearize(vsamples).data(), { m, n });
     }
     vector<pair<string, string>> Metrics::doCombinations(const vector<string>& source)
     {
@@ -19,7 +37,7 @@ namespace bayesnet {
         }
         return result;
     }
-    torch::Tensor Metrics::conditionalEdgeWeight()
+    vector<float> Metrics::conditionalEdgeWeights()
     {
         auto result = vector<double>();
         auto source = vector<string>(features);
@@ -54,7 +72,8 @@ namespace bayesnet {
             matrix[x][y] = result[i];
             matrix[y][x] = result[i];
         }
-        return matrix;
+        std::vector<float> v(matrix.data_ptr<float>(), matrix.data_ptr<float>() + matrix.numel());
+        return v;
     }
     double Metrics::entropy(torch::Tensor& feature)
     {

src/Metrics.hpp

@@ -7,7 +7,7 @@ using namespace std;
 namespace bayesnet {
     class Metrics {
     private:
-        torch::Tensor& samples;
+        torch::Tensor samples;
         vector<string>& features;
         string& className;
         int classNumStates;
@@ -17,7 +17,8 @@ namespace bayesnet {
         double mutualInformation(torch::Tensor&, torch::Tensor&);
     public:
         Metrics(torch::Tensor&, vector<string>&, string&, int);
-        torch::Tensor conditionalEdgeWeight();
+        Metrics(vector<vector<int>>&, int, int, vector<string>&, string&, int);
+        vector<float> conditionalEdgeWeights();
     };
 }
 #endif