Begin with parameter estimation

main.cc

@@ -11,10 +11,15 @@ using namespace std;
 vector<mdlp::labels_t> discretize(vector<mdlp::samples_t>& X, mdlp::labels_t& y)
 {
     vector<mdlp::labels_t>Xd;
+
     auto fimdlp = mdlp::CPPFImdlp();
     for (int i = 0; i < X.size(); i++) {
         fimdlp.fit(X[i], y);
-        Xd.push_back(fimdlp.transform(X[i]));
+        mdlp::labels_t& xd = fimdlp.transform(X[i]);
+        cout << "X[" << i << "]: ";
+        auto mm = minmax_element(xd.begin(), xd.end());
+        cout << *mm.first << " " << *mm.second << endl;
+        Xd.push_back(xd);
     }
     return Xd;
 }
@@ -33,7 +38,7 @@ int main()
         features.push_back(feature.first);
     }
     // Discretize Dataset
-    vector<mdlp::labels_t> Xd = discretize(X, y);;
+    vector<mdlp::labels_t> Xd = discretize(X, y);
     // Build Network    
     auto network = bayesnet::Network();
     network.fit(Xd, y, features, className);
@@ -53,6 +58,19 @@ int main()
     cout << "Root: " << network.getRoot()->getName() << endl;
     network.setRoot(className);
     cout << "Now Root should be class: " << network.getRoot()->getName() << endl;
+    cout << "CPDs:" << endl;
+    auto nodes = network.getNodes();
+    auto classNode = nodes[className];
+    for (auto it = nodes.begin(); it != nodes.end(); it++) {
+        cout << "* Name: " << it->first << " " << it->second->getName() << " -> " << it->second->getNumStates() << endl;
+        cout << "Parents: ";
+        for (auto parent : it->second->getParents()) {
+            cout << parent->getName() << " -> " << parent->getNumStates() << ", ";
+        }
+        cout << endl;
+        auto cpd = it->second->getCPT();
+        cout << cpd << endl;
+    }
     cout << "PyTorch version: " << TORCH_VERSION << endl;
     return 0;
 }