Trying to implement the learning of the error detection, some features missing

trainer/include/Trainer.hpp

@@ -63,6 +63,8 @@ class Trainer
   float currentSpeed;
   /// @brief The date the last time the speed has been computed.
   std::chrono::time_point<std::chrono::high_resolution_clock> pastTime;
+  /// @brief For each classifier, the last action applied and its cost.
+  std::map< std::string, std::vector <std::pair<std::string, int> > > lastActionTaken;
 
   public :
 

trainer/src/Trainer.cpp

@@ -287,7 +287,6 @@ void Trainer::doStepTrain()
   
     int k = ProgramParameters::dynamicEpoch;
   
-  
     if (ProgramParameters::featureExtraction)
     {
       auto features = tm.getCurrentClassifier()->getFeatureModel()->getFeatureDescription(trainConfig).featureValues();
@@ -347,50 +346,63 @@ void Trainer::doStepTrain()
       }
     }
 
-    //ici
-
-    float loss = 0.0;
-    if (!ProgramParameters::featureExtraction)
-      loss = tm.getCurrentClassifier()->trainOnExample(trainConfig, tm.getCurrentClassifier()->getActionIndex(oAction));
-  
-    TI.addTrainExample(tm.getCurrentClassifier()->name, loss);
-    if (pActionIsZeroCost)
-      TI.addTrainSuccess(tm.getCurrentClassifier()->name);
+    actionName = pAction;
 
-    int k = ProgramParameters::dynamicEpoch;
+    char buffer[1024];
+    if (sscanf(tm.getCurrentClassifier()->name.c_str(), "Error_%s", buffer) != 1)
+    {
+      fprintf(stderr, "ERROR (%s) : unexpected classifier name. Aborting.\n", ERRINFO);
+      exit(1);
+    }
 
+    auto & lastActionTakenError = lastActionTaken[tm.getCurrentClassifier()->name];
+    auto & lastActionTakenBase = lastActionTaken[buffer];
 
-    if (ProgramParameters::featureExtraction)
+    if (!lastActionTakenError.empty() && !lastActionTakenBase.empty())
     {
-      auto features = tm.getCurrentClassifier()->getFeatureModel()->getFeatureDescription(trainConfig).featureValues();
-      fprintf(stdout, "%s\t%s\n", oAction.c_str(), features.c_str());
+      if (lastActionTakenError.back().first != "EPSILON")
+      {
+        int sizeOfBack;
+        if (sscanf(lastActionTakenError.back().first.c_str(), "BACK %d", &sizeOfBack) != 1)
+        {
+          fprintf(stderr, "ERROR (%s) : unexpected classifier name. Aborting.\n", ERRINFO);
+          exit(1);
         }
+        auto & newAction = lastActionTakenBase.back().first;
+        auto & oldAction = lastActionTakenBase[lastActionTakenBase.size()-2-sizeOfBack].first;
+        auto & oldCost = lastActionTakenBase.back().second;
+        auto & newCost = lastActionTakenBase[lastActionTakenBase.size()-1].second;
 
-    if (TI.getEpoch() >= k && choiceWithProbability(ProgramParameters::dynamicProbability))
+        if (ProgramParameters::debug)
+          fprintf(stderr, "sizeOfBack %d <%s,%d> -> <%s,%d>\n", sizeOfBack, oldAction.c_str(), oldCost, newAction.c_str(), newCost);
+
+        if (newAction != oldAction)
         {
-      actionName = pAction;
+          fprintf(stderr, "sizeOfBack %d <%s,%d> -> <%s,%d>\n", sizeOfBack, oldAction.c_str(), oldCost, newAction.c_str(), newCost);
+          for (auto & it : lastActionTakenError)
+            fprintf(stderr, "<%s>\n", it.first.c_str());
+          fprintf(stderr, "-----\n");
+          //ici
+          //TODO : une fonction qui donne config.target() -> la case qui est focus, de plus on garde un historique de quelle est la derniere action a avoir modifié la case qui est sous le focus (enfin chaque case du coup)
+          exit(1);
+        }
       }
-    else
-    {
-      if (pActionIsZeroCost)
-        actionName = pAction;
-      else
-        actionName = oAction;
     }
   
     if (ProgramParameters::debug)
     {
       trainConfig.printForDebug(stderr);
+      tm.getCurrentClassifier()->printWeightedActions(stderr, weightedActions, 10);
       fprintf(stderr, "pAction=<%s> oAction=<%s> action=<%s>\n", pAction.c_str(), oAction.c_str(), actionName.c_str());
     }
 
-    actionName = "BANANE";
   }
 
   Action * action = tm.getCurrentClassifier()->getAction(actionName);
   TransitionMachine::Transition * transition = tm.getTransition(actionName);
   action->setInfos(transition->headMvt, tm.getCurrentState());
 
+  lastActionTaken[tm.getCurrentClassifier()->name].emplace_back(actionName, tm.getCurrentClassifier()->getActionCost(trainConfig, actionName));
   action->apply(trainConfig);
   tm.takeTransition(transition);