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disabled the "conservative" choice selection for value iteration because it produced wrong results (and we don't need this anymore) 

Former-commit-id: 9a8cced48f
main
TimQu 9 years ago
parent
e6c89a6f45
commit
f442d9a434
2 changed files with 26 additions and 53 deletions
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  1. 34
      src/modelchecker/multiobjective/helper/SparseMultiObjectiveWeightVectorChecker.cpp
  2. 45
      src/solver/GmmxxMinMaxLinearEquationSolver.cpp

34
src/modelchecker/multiobjective/helper/SparseMultiObjectiveWeightVectorChecker.cpp
View File

@ -25,20 +25,14 @@ namespace storm {
template <class SparseModelType>
void SparseMultiObjectiveWeightVectorChecker<SparseModelType>::check(std::vector<ValueType> const& weightVector) {
//STORM_LOG_DEBUG("Checking weighted objectives with weight vector " << std::endl << "\t" << weightVector);
checkHasBeenCalled=true;
STORM_LOG_DEBUG("Invoked WeightVectorChecker with weights " << std::endl << "\t" << weightVector);
unboundedWeightedPhase(weightVector);
//STORM_LOG_DEBUG("Unbounded weighted phase resulted in " << std::endl << "\t Result: " << weightedResult << std::endl << "\t Scheduler: " << scheduler);
STORM_LOG_DEBUG("Unbounded weighted phase result: " << weightedResult[data.preprocessedModel.getInitialStates().getNextSetIndex(0)] << " (value in initial state).");
unboundedIndividualPhase(weightVector);
//STORM_LOG_DEBUG("Unbounded individual phase resulted in...");
for(uint_fast64_t objIndex = 0; objIndex < data.objectives.size(); ++objIndex) {
// STORM_LOG_DEBUG("\t objective " << objIndex << ":" << objectiveResults[objIndex]);
}
STORM_LOG_DEBUG("Unbounded individual phase results in initial state: " << getInitialStateResultOfObjectives());
boundedPhase(weightVector);
// STORM_LOG_DEBUG("Bounded phase resulted in...");
for(uint_fast64_t objIndex = 0; objIndex < data.objectives.size(); ++objIndex) {
// STORM_LOG_DEBUG("\t objective " << objIndex << ":" << objectiveResults[objIndex]);
}
checkHasBeenCalled=true;
STORM_LOG_DEBUG("Bounded individual phase results in initial state: " << getInitialStateResultOfObjectives() << " ...WeightVectorChecker done.");
}
template <class SparseModelType>
@ -146,19 +140,15 @@ namespace storm {
//Also only compute values for objectives with weightVector != zero,
//one check can be omitted as the result can be computed back from the weighed result and the results from the remaining objectives
for(uint_fast64_t objIndex = 0; objIndex < weightVector.size(); ++objIndex) {
if(!data.objectives[objIndex].stepBound){
if(data.objectives[objIndex].stepBound){
objectiveResults[objIndex] = std::vector<ValueType>(data.preprocessedModel.getNumberOfStates(), storm::utility::zero<ValueType>());
} else {
storm::utility::vector::selectVectorValues(deterministicStateRewards, this->scheduler.getChoices(), data.preprocessedModel.getTransitionMatrix().getRowGroupIndices(), data.preprocessedModel.getRewardModel(data.objectives[objIndex].rewardModelName).getStateActionRewardVector());
//std::cout << "stateActionRewardVector for objective " << objIndex << " is " << storm::utility::vector::toString(data.preprocessedModel.getRewardModel(data.objectives[objIndex].rewardModelName).getStateActionRewardVector()) << std::endl;
//std::cout << "deterministic state rewards for objective " << objIndex << " are " << storm::utility::vector::toString(deterministicStateRewards) << std::endl;
storm::storage::BitVector statesWithRewards = ~storm::utility::vector::filterZero(deterministicStateRewards);
// As target states, we take the states from which no reward is reachable.
STORM_LOG_WARN("TODO: target state selection is currently only valid for reachability properties...");
//TODO: we should be able to give some hint to the solver..
storm::storage::BitVector targetStates = storm::utility::graph::performProbGreater0(deterministicBackwardTransitions, storm::storage::BitVector(deterministicMatrix.getRowCount(), true), statesWithRewards);
targetStates.complement();
// As target states, we pick the states from which no reward is reachable.
storm::storage::BitVector targetStates = ~storm::utility::graph::performProbGreater0(deterministicBackwardTransitions, storm::storage::BitVector(deterministicMatrix.getRowCount(), true), statesWithRewards);
//TODO: we could give the solver some hint for the result (e.g., weightVector[ObjIndex] * weightedResult or (weightedResult - sum_i=0^objIndex-1 objectiveResult) * weightVector[objIndex]/ sum_i=objIndex^n weightVector[i] )
objectiveResults[objIndex] = storm::modelchecker::helper::SparseDtmcPrctlHelper<ValueType>::computeReachabilityRewards(deterministicMatrix,
deterministicBackwardTransitions,
deterministicStateRewards,

45
src/solver/GmmxxMinMaxLinearEquationSolver.cpp
View File

@ -42,14 +42,6 @@ namespace storm {
newX = new std::vector<ValueType>(x.size());
xMemoryProvided = false;
}
std::vector<storm::storage::sparse::state_type> currentScheduler;
std::vector<storm::storage::sparse::state_type> newScheduler;
if(this->trackScheduler){
//Allocate memory for the schedulers and initialize them with valid choices
currentScheduler = std::vector<storm::storage::sparse::state_type>(this->A.getRowGroupCount());
newScheduler = std::vector<storm::storage::sparse::state_type>(this->A.getRowGroupCount());
}
uint_fast64_t iterations = 0;
bool converged = false;
@ -63,24 +55,8 @@ namespace storm {
gmm::mult(*gmmxxMatrix, *currentX, *multiplyResult);
gmm::add(b, *multiplyResult);
if(this->trackScheduler){
// Reduce the vector x by applying min/max over all nondeterministic choices. Also keep track of the choices made
storm::utility::vector::reduceVectorMinOrMax(dir, *multiplyResult, *newX, rowGroupIndices, &newScheduler);
// We update the currentScheduler conservatively
// This is to prevent that e.g. a choice for a selfloop with probability 1 is taken
auto currentChoice = currentScheduler.begin();
auto newChoice = newScheduler.begin();
for(storm::storage::sparse::state_type state = 0; state<currentScheduler.size(); ++state) {
if(*currentChoice != *newChoice && !storm::utility::vector::equalModuloPrecision((*multiplyResult)[state + (*currentChoice)], (*multiplyResult)[state + (*newChoice)], this->precision, this->relative)){
*currentChoice = *newChoice;
}
++currentChoice;
++newChoice;
}
} else {
// Reduce the vector x by applying min/max over all nondeterministic choices.
storm::utility::vector::reduceVectorMinOrMax(dir, *multiplyResult, *newX, rowGroupIndices);
}
// Reduce the vector x by applying min/max over all nondeterministic choices.
storm::utility::vector::reduceVectorMinOrMax(dir, *multiplyResult, *newX, rowGroupIndices);
// Determine whether the method converged.
converged = storm::utility::vector::equalModuloPrecision(*currentX, *newX, this->precision, this->relative);
@ -96,17 +72,24 @@ namespace storm {
} else {
STORM_LOG_WARN("Iterative solver did not converge after " << iterations << " iterations.");
}
// If requested, we store the scheduler for retrieval.
if (this->isTrackSchedulerSet()) {
this->scheduler = std::make_unique<storm::storage::TotalScheduler>(std::move(currentScheduler));
}
// If we performed an odd number of iterations, we need to swap the x and currentX, because the newest result
// is currently stored in currentX, but x is the output vector.
if (currentX == copyX) {
std::swap(x, *currentX);
}
// If requested, we store the scheduler for retrieval.
if (this->isTrackSchedulerSet()) {
if(iterations==0){ //may happen due to custom termination condition. Then we need to compute x'= A*x+b
gmm::mult(*gmmxxMatrix, x, *multiplyResult);
gmm::add(b, *multiplyResult);
}
std::vector<storm::storage::sparse::state_type> choices(this->A.getRowGroupCount());
// Reduce the multiplyResult again and keep track of the choices made
storm::utility::vector::reduceVectorMinOrMax(dir, *multiplyResult, x, rowGroupIndices, &choices);
this->scheduler = std::make_unique<storm::storage::TotalScheduler>(std::move(choices));
}
if (!xMemoryProvided) {
delete copyX;

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