ObjectiveHelper: Fixed wrong rewards with Markov Automata.

src/storm/modelchecker/multiobjective/deterministicScheds/DeterministicSchedsObjectiveHelper.cpp

@@ -44,6 +44,26 @@ namespace storm {
                 return checkResult->asExplicitQualitativeCheckResult().getTruthValuesVector();
             }
             
+            template <typename ValueType>
+            std::vector<ValueType> getTotalRewardVector(storm::models::sparse::MarkovAutomaton<ValueType> const& model, storm::logic::Formula const& formula) {
+                boost::optional<std::string> rewardModelName = formula.asRewardOperatorFormula().getOptionalRewardModelName();
+                typename storm::models::sparse::MarkovAutomaton<ValueType>::RewardModelType const& rewardModel = rewardModelName.is_initialized() ? model.getRewardModel(rewardModelName.get()) : model.getUniqueRewardModel();
+
+                // Get a reward model where the state rewards are scaled accordingly
+                std::vector<ValueType> stateRewardWeights(model.getNumberOfStates(), storm::utility::zero<ValueType>());
+                for (auto const markovianState : model.getMarkovianStates()) {
+                    stateRewardWeights[markovianState] = storm::utility::one<ValueType>() / model.getExitRate(markovianState);
+                }
+                return rewardModel.getTotalActionRewardVector(model.getTransitionMatrix(), stateRewardWeights);
+            }
+            
+            template <typename ValueType>
+            std::vector<ValueType> getTotalRewardVector(storm::models::sparse::Mdp<ValueType> const& model, storm::logic::Formula const& formula) {
+                boost::optional<std::string> rewardModelName = formula.asRewardOperatorFormula().getOptionalRewardModelName();
+                typename storm::models::sparse::Mdp<ValueType>::RewardModelType const& rewardModel = rewardModelName.is_initialized() ? model.getRewardModel(rewardModelName.get()) : model.getUniqueRewardModel();
+                return rewardModel.getTotalRewardVector(model.getTransitionMatrix());
+            }
+            
             template <typename ModelType>
             std::map<uint64_t, typename ModelType::ValueType> const& DeterministicSchedsObjectiveHelper<ModelType>::getSchedulerIndependentStateValues() const {
                 if (!schedulerIndependentStateValues) {
@@ -104,7 +124,8 @@ namespace storm {
                     } else if (formula.isRewardOperatorFormula() && (subformula.isTotalRewardFormula() || subformula.isEventuallyFormula())) {
                         auto const& baseRewardModel = formula.asRewardOperatorFormula().hasRewardModelName() ? model.getRewardModel(formula.asRewardOperatorFormula().getRewardModelName()) : model.getUniqueRewardModel();
                         auto rewardModel = subformula.isEventuallyFormula() ? storm::utility::createFilteredRewardModel(baseRewardModel, model.isDiscreteTimeModel(), subformula.asEventuallyFormula()) : storm::utility::createFilteredRewardModel(baseRewardModel, model.isDiscreteTimeModel(), subformula.asTotalRewardFormula());
-                        std::vector<ValueType> choiceBasedRewards = rewardModel.get().getTotalRewardVector(model.getTransitionMatrix());
+                        std::vector<ValueType> choiceBasedRewards = getTotalRewardVector(model, *objective.formula);
+                        
                         // Set entries for all non-zero reward choices at states whose value is not already known.
                         // This relies on the fact that for goal states in reachability reward formulas, getSchedulerIndependentStateValues()[state] is set to zero.
                         auto const& rowGroupIndices = model.getTransitionMatrix().getRowGroupIndices();
@@ -128,7 +149,6 @@ namespace storm {
                             rates = &ma->getExitRates();
                             ms = &ma->getMarkovianStates();
                         }
-                        if (model.isOfType(storm::models::ModelType::Mdp)) {
                         // Set all choice offsets to one, except for the ones at states in scheduerIndependentStateValues.
                         for (uint64_t state = 0; state < model.getNumberOfStates(); ++state) {
                             if (stateValues.find(state) == stateValues.end()) {
@@ -146,7 +166,6 @@ namespace storm {
                                 }
                             }
                         }
-                        }
                     } else {
                         STORM_LOG_THROW(false, storm::exceptions::NotSupportedException, "The given formula " << formula << " is not supported.");
                     }
@@ -200,26 +219,6 @@ namespace storm {
                 }
             }
             
-            template <typename ValueType>
-            std::vector<ValueType> getTotalRewardVector(storm::models::sparse::MarkovAutomaton<ValueType> const& model, storm::logic::Formula const& formula) {
-                boost::optional<std::string> rewardModelName = formula.asRewardOperatorFormula().getOptionalRewardModelName();
-                typename storm::models::sparse::MarkovAutomaton<ValueType>::RewardModelType const& rewardModel = rewardModelName.is_initialized() ? model.getRewardModel(rewardModelName.get()) : model.getUniqueRewardModel();
-
-                // Get a reward model where the state rewards are scaled accordingly
-                std::vector<ValueType> stateRewardWeights(model.getNumberOfStates(), storm::utility::zero<ValueType>());
-                for (auto const markovianState : model.getMarkovianStates()) {
-                    stateRewardWeights[markovianState] = storm::utility::one<ValueType>() / model.getExitRate(markovianState);
-                }
-                return rewardModel.getTotalActionRewardVector(model.getTransitionMatrix(), stateRewardWeights);
-            }
-            
-            template <typename ValueType>
-            std::vector<ValueType> getTotalRewardVector(storm::models::sparse::Mdp<ValueType> const& model, storm::logic::Formula const& formula) {
-                boost::optional<std::string> rewardModelName = formula.asRewardOperatorFormula().getOptionalRewardModelName();
-                typename storm::models::sparse::Mdp<ValueType>::RewardModelType const& rewardModel = rewardModelName.is_initialized() ? model.getRewardModel(rewardModelName.get()) : model.getUniqueRewardModel();
-                return rewardModel.getTotalRewardVector(model.getTransitionMatrix());
-            }
-            
             template <typename ModelType>
             typename ModelType::ValueType const& DeterministicSchedsObjectiveHelper<ModelType>::getUpperValueBoundAtState(Environment const& env, uint64_t state) const {
                 computeUpperBounds(env);