Refactor constructor

sample/main.cc

@@ -231,6 +231,7 @@ int main(int argc, char** argv)
     cout << "BayesNet version: " << network.version() << endl;
     unsigned int nthreads = std::thread::hardware_concurrency();
     cout << "Computer has " << nthreads << " cores." << endl;
+    cout << "****************** First ******************" << endl;
     auto metrics = bayesnet::Metrics(network.getSamples(), features, className, network.getClassNumStates());
     cout << "conditionalEdgeWeight " << endl;
     auto conditional = metrics.conditionalEdgeWeights();
@@ -238,5 +239,13 @@ int main(int argc, char** argv)
     long m = features.size() + 1;
     auto matrix = torch::from_blob(conditional.data(), { m, m });
     cout << matrix << endl;
+    cout << "****************** Second ******************" << endl;
+    auto metrics2 = bayesnet::Metrics(Xd, y, features, className, network.getClassNumStates());
+    cout << "conditionalEdgeWeight " << endl;
+    auto conditional2 = metrics2.conditionalEdgeWeights();
+    cout << conditional2 << endl;
+    long m2 = features.size() + 1;
+    auto matrix2 = torch::from_blob(conditional2.data(), { m, m });
+    cout << matrix2 << endl;
     return 0;
 }

src/Metrics.cc

@@ -1,30 +1,23 @@
 #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)
+    Metrics::Metrics(vector<vector<int>>& vsamples, vector<int>& labels, vector<string>& features, string& className, int classNumStates)
         : features(features)
         , className(className)
         , classNumStates(classNumStates)
     {
-        samples = torch::from_blob(linearize(vsamples).data(), { m, n });
+        samples = torch::zeros({ static_cast<int64_t>(vsamples[0].size()), static_cast<int64_t>(vsamples.size() + 1) }, torch::kInt64);
+        for (int i = 0; i < vsamples.size(); ++i) {
+            samples.index_put_({ "...", i }, torch::tensor(vsamples[i], torch::kInt64));
+        }
+        samples.index_put_({ "...", -1 }, torch::tensor(labels, torch::kInt64));
     }
     vector<pair<string, string>> Metrics::doCombinations(const vector<string>& source)
     {

src/Metrics.hpp

@@ -17,7 +17,7 @@ namespace bayesnet {
         double mutualInformation(torch::Tensor&, torch::Tensor&);
     public:
         Metrics(torch::Tensor&, vector<string>&, string&, int);
-        Metrics(vector<vector<int>>&, int, int, vector<string>&, string&, int);
+        Metrics(vector<vector<int>>&, vector<int>&, vector<string>&, string&, int);
         vector<float> conditionalEdgeWeights();
     };
 }