Refactor coverage report generation

Add some tests to reach 99%

bayesnet/ensembles/A2DE.cc

@@ -10,7 +10,6 @@ namespace bayesnet {
     A2DE::A2DE(bool predict_voting) : Ensemble(predict_voting)
     {
         validHyperparameters = { "predict_voting" };
-
     }
     void A2DE::setHyperparameters(const nlohmann::json& hyperparameters_)
     {

bayesnet/network/Node.cc

@@ -9,7 +9,7 @@
 namespace bayesnet {
 
     Node::Node(const std::string& name)
-        : name(name), numStates(0), cpTable(torch::Tensor()), parents(std::vector<Node*>()), children(std::vector<Node*>())
+        : name(name)
     {
     }
     void Node::clear()
@@ -96,7 +96,6 @@ namespace bayesnet {
         // Get dimensions of the CPT
         dimensions.push_back(numStates);
         transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
-
         // Create a tensor of zeros with the dimensions of the CPT
         cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
         // Fill table with counts

bayesnet/network/Node.h

@@ -12,14 +12,6 @@
 #include <torch/torch.h>
 namespace bayesnet {
     class Node {
-    private:
-        std::string name;
-        std::vector<Node*> parents;
-        std::vector<Node*> children;
-        int numStates; // number of states of the variable
-        torch::Tensor cpTable; // Order of indices is 0-> node variable, 1-> 1st parent, 2-> 2nd parent, ...
-        std::vector<int64_t> dimensions; // dimensions of the cpTable
-        std::vector<std::pair<std::string, std::string>> combinations(const std::vector<std::string>&);
     public:
         explicit Node(const std::string&);
         void clear();
@@ -37,6 +29,14 @@ namespace bayesnet {
         unsigned minFill();
         std::vector<std::string> graph(const std::string& clasName); // Returns a std::vector of std::strings representing the graph in graphviz format
         float getFactorValue(std::map<std::string, int>&);
+    private:
+        std::string name;
+        std::vector<Node*> parents;
+        std::vector<Node*> children;
+        int numStates = 0; // number of states of the variable
+        torch::Tensor cpTable; // Order of indices is 0-> node variable, 1-> 1st parent, 2-> 2nd parent, ...
+        std::vector<int64_t> dimensions; // dimensions of the cpTable
+        std::vector<std::pair<std::string, std::string>> combinations(const std::vector<std::string>&);
     };
 }
 #endif