adapted mdpprctlhelper call in MA model checker

src/storm/modelchecker/csl/SparseMarkovAutomatonCslModelChecker.cpp

@@ -29,7 +29,7 @@ namespace storm {
         SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::SparseMarkovAutomatonCslModelChecker(SparseMarkovAutomatonModelType const& model) : SparsePropositionalModelChecker<SparseMarkovAutomatonModelType>(model) {
             // Intentionally left empty.
         }
-        
+
         template <typename ModelType>
         bool SparseMarkovAutomatonCslModelChecker<ModelType>::canHandleStatic(CheckTask<storm::logic::Formula, ValueType> const& checkTask, bool* requiresSingleInitialState) {
             auto singleObjectiveFragment = storm::logic::csrlstar().setRewardOperatorsAllowed(true).setReachabilityRewardFormulasAllowed(true).setTotalRewardFormulasAllowed(true).setTimeAllowed(true).setLongRunAverageProbabilitiesAllowed(true).setLongRunAverageRewardFormulasAllowed(true).setRewardAccumulationAllowed(true).setInstantaneousFormulasAllowed(false);
@@ -48,7 +48,7 @@ namespace storm {
             }
             return false;
         }
-        
+
         template<typename SparseMarkovAutomatonModelType>
         bool SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::canHandle(CheckTask<storm::logic::Formula, ValueType> const& checkTask) const {
             bool requiresSingleInitialState = false;
@@ -58,7 +58,7 @@ namespace storm {
                 return false;
             }
         }
-        
+
         template<typename SparseMarkovAutomatonModelType>
         std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::computeBoundedUntilProbabilities(Environment const& env, CheckTask<storm::logic::BoundedUntilFormula, ValueType> const& checkTask) {
             storm::logic::BoundedUntilFormula const& pathFormula = checkTask.getFormula();
@@ -85,18 +85,18 @@ namespace storm {
             std::vector<ValueType> result = storm::modelchecker::helper::SparseMarkovAutomatonCslHelper::computeBoundedUntilProbabilities(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), this->getModel().getTransitionMatrix(), this->getModel().getExitRates(), this->getModel().getMarkovianStates(), leftResult.getTruthValuesVector(), rightResult.getTruthValuesVector(), std::make_pair(lowerBound, upperBound));
             return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(result)));
         }
-        
-        
+
+
         template<typename SparseMarkovAutomatonModelType>
         std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::computeNextProbabilities(Environment const& env, CheckTask<storm::logic::NextFormula, ValueType> const& checkTask) {
             storm::logic::NextFormula const& pathFormula = checkTask.getFormula();
             STORM_LOG_THROW(checkTask.isOptimizationDirectionSet(), storm::exceptions::InvalidPropertyException, "Formula needs to specify whether minimal or maximal values are to be computed on nondeterministic model.");
             std::unique_ptr<CheckResult> subResultPointer = this->check(env, pathFormula.getSubformula());
             ExplicitQualitativeCheckResult const& subResult = subResultPointer->asExplicitQualitativeCheckResult();
-            std::vector<ValueType> numericResult = storm::modelchecker::helper::SparseMdpPrctlHelper<ValueType>::computeNextProbabilities(env, checkTask.getOptimizationDirection(), this->getModel().getTransitionMatrix(), subResult.getTruthValuesVector());
-            return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(numericResult)));
+            auto ret = storm::modelchecker::helper::SparseMdpPrctlHelper<ValueType>::computeNextProbabilities(env, storm::solver::SolveGoal<ValueType>(this->getModel(), checkTask), checkTask.getOptimizationDirection(), this->getModel().getTransitionMatrix(), subResult.getTruthValuesVector());
+            return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(ret.values)));
         }
-        
+
         template<typename SparseMarkovAutomatonModelType>
         std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::computeGloballyProbabilities(Environment const& env, CheckTask<storm::logic::GloballyFormula, ValueType> const& checkTask) {
             storm::logic::GloballyFormula const& pathFormula = checkTask.getFormula();
@@ -110,7 +110,7 @@ namespace storm {
             }
             return result;
         }
-                
+
         template<typename SparseMarkovAutomatonModelType>
         std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::computeUntilProbabilities(Environment const& env, CheckTask<storm::logic::UntilFormula, ValueType> const& checkTask) {
             storm::logic::UntilFormula const& pathFormula = checkTask.getFormula();
@@ -131,14 +131,14 @@ namespace storm {
         template<typename SparseMarkovAutomatonModelType>
         std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::computeHOAPathProbabilities(Environment const& env, CheckTask<storm::logic::HOAPathFormula, ValueType> const& checkTask) {
             storm::logic::HOAPathFormula const& pathFormula = checkTask.getFormula();
-            
+
             storm::modelchecker::helper::SparseLTLHelper<ValueType, true> helper(this->getModel().getTransitionMatrix());
             storm::modelchecker::helper::setInformationFromCheckTaskNondeterministic(helper, checkTask, this->getModel());
-            
+
             auto formulaChecker = [&] (storm::logic::Formula const& formula) { return this->check(env, formula)->asExplicitQualitativeCheckResult().getTruthValuesVector(); };
             auto apSets = helper.computeApSets(pathFormula.getAPMapping(), formulaChecker);
             std::vector<ValueType> numericResult = helper.computeDAProductProbabilities(env, *pathFormula.readAutomaton(), apSets);
-            
+
             std::unique_ptr<CheckResult> result(new ExplicitQuantitativeCheckResult<ValueType>(std::move(numericResult)));
             if (checkTask.isProduceSchedulersSet()) {
                 result->asExplicitQuantitativeCheckResult<ValueType>().setScheduler(std::make_unique<storm::storage::Scheduler<ValueType>>(helper.extractScheduler(this->getModel())));
@@ -146,7 +146,7 @@ namespace storm {
 
             return result;
         }
-        
+
         template<typename SparseMarkovAutomatonModelType>
         std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::computeLTLProbabilities(Environment const& env, CheckTask<storm::logic::PathFormula, ValueType> const& checkTask) {
             storm::logic::PathFormula const& pathFormula = checkTask.getFormula();
@@ -155,7 +155,7 @@ namespace storm {
 
             storm::modelchecker::helper::SparseLTLHelper<ValueType, true> helper(this->getModel().getTransitionMatrix());
             storm::modelchecker::helper::setInformationFromCheckTaskNondeterministic(helper, checkTask, this->getModel());
-            
+
             auto formulaChecker = [&] (storm::logic::Formula const& formula) { return this->check(env, formula)->asExplicitQualitativeCheckResult().getTruthValuesVector(); };
             std::vector<ValueType> numericResult = helper.computeLTLProbabilities(env, pathFormula, formulaChecker);
 
@@ -166,7 +166,7 @@ namespace storm {
 
             return result;
         }
-        
+
         template<typename SparseMarkovAutomatonModelType>
         std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::computeReachabilityRewards(Environment const& env, storm::logic::RewardMeasureType, CheckTask<storm::logic::EventuallyFormula, ValueType> const& checkTask) {
             storm::logic::EventuallyFormula const& eventuallyFormula = checkTask.getFormula();
@@ -183,7 +183,7 @@ namespace storm {
             }
             return result;
         }
-        
+
         template<typename SparseMarkovAutomatonModelType>
         std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::computeTotalRewards(Environment const& env, storm::logic::RewardMeasureType, CheckTask<storm::logic::TotalRewardFormula, ValueType> const& checkTask) {
             STORM_LOG_THROW(checkTask.isOptimizationDirectionSet(), storm::exceptions::InvalidPropertyException, "Formula needs to specify whether minimal or maximal values are to be computed on nondeterministic model.");
@@ -197,7 +197,7 @@ namespace storm {
             }
             return result;
         }
-        
+
         template<typename SparseMarkovAutomatonModelType>
         std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::computeLongRunAverageProbabilities(Environment const& env, CheckTask<storm::logic::StateFormula, ValueType> const& checkTask) {
             storm::logic::StateFormula const& stateFormula = checkTask.getFormula();
@@ -216,13 +216,13 @@ namespace storm {
             }
             return result;
         }
-        
+
         template<typename SparseMarkovAutomatonModelType>
         std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::computeLongRunAverageRewards(Environment const& env, storm::logic::RewardMeasureType rewardMeasureType, CheckTask<storm::logic::LongRunAverageRewardFormula, ValueType> const& checkTask) {
             STORM_LOG_THROW(checkTask.isOptimizationDirectionSet(), storm::exceptions::InvalidPropertyException, "Formula needs to specify whether minimal or maximal values are to be computed on nondeterministic model.");
             STORM_LOG_THROW(this->getModel().isClosed(), storm::exceptions::InvalidPropertyException, "Unable to compute long run average rewards in non-closed Markov automaton.");
             auto rewardModel = storm::utility::createFilteredRewardModel(this->getModel(), checkTask);
-            
+
             storm::modelchecker::helper::SparseNondeterministicInfiniteHorizonHelper<ValueType> helper(this->getModel().getTransitionMatrix(), this->getModel().getMarkovianStates(), this->getModel().getExitRates());
             storm::modelchecker::helper::setInformationFromCheckTaskNondeterministic(helper, checkTask, this->getModel());
             auto values = helper.computeLongRunAverageRewards(env, rewardModel.get());
@@ -233,7 +233,7 @@ namespace storm {
             }
             return result;
         }
-        
+
         template<typename SparseMarkovAutomatonModelType>
         std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::computeReachabilityTimes(Environment const& env, storm::logic::RewardMeasureType, CheckTask<storm::logic::EventuallyFormula, ValueType> const& checkTask) {
             storm::logic::EventuallyFormula const& eventuallyFormula = checkTask.getFormula();
@@ -249,12 +249,12 @@ namespace storm {
             }
             return result;
         }
-        
+
         template<typename SparseMarkovAutomatonModelType>
         std::unique_ptr<CheckResult> SparseMarkovAutomatonCslModelChecker<SparseMarkovAutomatonModelType>::checkMultiObjectiveFormula(Environment const& env, CheckTask<storm::logic::MultiObjectiveFormula, ValueType> const& checkTask) {
             return multiobjective::performMultiObjectiveModelChecking(env, this->getModel(), checkTask.getFormula());
         }
-        
+
         template class SparseMarkovAutomatonCslModelChecker<storm::models::sparse::MarkovAutomaton<double>>;
         template class SparseMarkovAutomatonCslModelChecker<storm::models::sparse::MarkovAutomaton<storm::RationalNumber>>;
     }