Refactor BaseClassifier and begin TAN impl.

src/BaseClassifier.cc

@@ -4,7 +4,7 @@ namespace bayesnet {
     using namespace std;
     using namespace torch;
 
-    BaseClassifier::BaseClassifier(Network model) : model(model), m(0), n(0) {}
+    BaseClassifier::BaseClassifier(Network model) : model(model), m(0), n(0), metrics(Metrics()) {}
     BaseClassifier& BaseClassifier::build(vector<string>& features, string className, map<string, vector<int>>& states)
     {
 
@@ -13,6 +13,8 @@ namespace bayesnet {
         this->className = className;
         this->states = states;
         checkFitParameters();
+        auto n_classes = states[className].size();
+        metrics = Metrics(dataset, features, className, n_classes);
         train();
         return *this;
     }
@@ -51,6 +53,14 @@ namespace bayesnet {
             }
         }
     }
+    vector<int> BaseClassifier::argsort(vector<float>& nums)
+    {
+        int n = nums.size();
+        vector<int> indices(n);
+        iota(indices.begin(), indices.end(), 0);
+        sort(indices.begin(), indices.end(), [&nums](int i, int j) {return nums[i] > nums[j];});
+        return indices;
+    }
     vector<vector<int>> tensorToVector(const torch::Tensor& tensor)
     {
         // convert mxn tensor to nxm vector
@@ -86,8 +96,16 @@ namespace bayesnet {
         Tensor y_pred = predict(X);
         return (y_pred == y).sum().item<float>() / y.size(0);
     }
-    void BaseClassifier::show()
+    vector<string> BaseClassifier::show()
     {
-        model.show();
+        return model.show();
+    }
+    void BaseClassifier::addNodes()
+    {
+        // Add all nodes to the network
+        for (auto feature : features) {
+            model.addNode(feature, states[feature].size());
+        }
+        model.addNode(className, states[className].size());
     }
 }