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Merge remote-tracking branch 'upstream/master'

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Matthias Volk 8 years ago
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ed53c9287f 752af135cd
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9611a1b243
2 changed files with 11 additions and 124 deletions
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  1. 119
      src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.cpp
  2. 16
      src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.h

119
src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.cpp
View File

@ -200,12 +200,16 @@ namespace storm {
template <typename ValueType, typename RewardModelType>
std::vector<ValueType> SparseMarkovAutomatonCslHelper::computeReachabilityRewards(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, std::vector<ValueType> const& exitRateVector, storm::storage::BitVector const& markovianStates, RewardModelType const& rewardModel, storm::storage::BitVector const& psiStates, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory) {
std::vector<ValueType> stateRewardWeights(transitionMatrix.getRowGroupCount());
// Get a reward model where the state rewards are scaled accordingly
std::vector<ValueType> stateRewardWeights(transitionMatrix.getRowGroupCount(), storm::utility::zero<ValueType>());
for (auto const markovianState : markovianStates) {
stateRewardWeights[markovianState] = storm::utility::one<ValueType>() / exitRateVector[markovianState];
}
std::vector<ValueType> totalRewardVector = rewardModel.getTotalActionRewardVector(transitionMatrix, stateRewardWeights);
return computeExpectedRewards(dir, transitionMatrix, backwardTransitions, psiStates, totalRewardVector, minMaxLinearEquationSolverFactory);
RewardModelType scaledRewardModel(boost::none, std::move(totalRewardVector));
return SparseMdpPrctlHelper<ValueType>::computeReachabilityRewards(dir, transitionMatrix, backwardTransitions, scaledRewardModel, psiStates, false, false, minMaxLinearEquationSolverFactory).values;
}
template<typename ValueType>
@ -365,112 +369,15 @@ namespace storm {
template <typename ValueType>
std::vector<ValueType> SparseMarkovAutomatonCslHelper::computeReachabilityTimes(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, std::vector<ValueType> const& exitRateVector, storm::storage::BitVector const& markovianStates, storm::storage::BitVector const& psiStates, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory) {
// Get a reward model representing expected sojourn times
std::vector<ValueType> rewardValues(transitionMatrix.getRowCount(), storm::utility::zero<ValueType>());
for (auto const markovianState : markovianStates) {
rewardValues[transitionMatrix.getRowGroupIndices()[markovianState]] = storm::utility::one<ValueType>() / exitRateVector[markovianState];
}
return computeExpectedRewards(dir, transitionMatrix, backwardTransitions, psiStates, rewardValues, minMaxLinearEquationSolverFactory);
}
template<typename ValueType>
std::vector<ValueType> SparseMarkovAutomatonCslHelper::computeExpectedRewards(OptimizationDirection dir,
storm::storage::SparseMatrix<ValueType> const &transitionMatrix,
storm::storage::SparseMatrix<ValueType> const &backwardTransitions,
storm::storage::BitVector const &goalStates,
std::vector<ValueType> const &stateActionRewardVector,
storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const &minMaxLinearEquationSolverFactory) {
uint_fast64_t numberOfStates = transitionMatrix.getRowGroupCount();
// First, we need to check which states have infinite expected time (by definition).
storm::storage::BitVector infinityStates;
if (dir == OptimizationDirection::Minimize) {
// If we need to compute the minimum expected times, we have to set the values of those states to infinity that, under all schedulers,
// reach a bottom SCC without a goal state.
// So we start by computing all bottom SCCs without goal states.
storm::storage::StronglyConnectedComponentDecomposition<ValueType> sccDecomposition(transitionMatrix,
~goalStates, true,
true);
// Now form the union of all these SCCs.
storm::storage::BitVector unionOfNonGoalBSccs(numberOfStates);
for (auto const &scc : sccDecomposition) {
for (auto state : scc) {
unionOfNonGoalBSccs.set(state);
}
}
// Finally, if this union is non-empty, compute the states such that all schedulers reach some state of the union.
if (!unionOfNonGoalBSccs.empty()) {
infinityStates = storm::utility::graph::performProbGreater0A(transitionMatrix,
transitionMatrix.getRowGroupIndices(),
backwardTransitions,
storm::storage::BitVector(
numberOfStates, true),
unionOfNonGoalBSccs);
} else {
// Otherwise, we have no infinity states.
infinityStates = storm::storage::BitVector(numberOfStates);
}
} else {
// If we maximize the property, the expected time of a state is infinite, if an end-component without any goal state is reachable.
// So we start by computing all MECs that have no goal state.
storm::storage::MaximalEndComponentDecomposition<ValueType> mecDecomposition(transitionMatrix,
backwardTransitions,
~goalStates);
// Now we form the union of all states in these end components.
storm::storage::BitVector unionOfNonGoalMaximalEndComponents(numberOfStates);
for (auto const &mec : mecDecomposition) {
for (auto const &stateActionPair : mec) {
unionOfNonGoalMaximalEndComponents.set(stateActionPair.first);
}
}
if (!unionOfNonGoalMaximalEndComponents.empty()) {
// Now we need to check for which states there exists a scheduler that reaches one of the previously computed states.
infinityStates = storm::utility::graph::performProbGreater0E(backwardTransitions,
storm::storage::BitVector(
numberOfStates, true),
unionOfNonGoalMaximalEndComponents);
} else {
// Otherwise, we have no infinity states.
infinityStates = storm::storage::BitVector(numberOfStates);
}
}
// Now we identify the states for which values need to be computed.
storm::storage::BitVector maybeStates = ~(goalStates | infinityStates);
// Create resulting vector.
std::vector<ValueType> result(numberOfStates);
if (!maybeStates.empty()) {
// Then, we can eliminate the rows and columns for all states whose values are already known.
std::vector<ValueType> x(maybeStates.getNumberOfSetBits());
storm::storage::SparseMatrix<ValueType> submatrix = transitionMatrix.getSubmatrix(true, maybeStates,
maybeStates);
// Finally, prepare the actual right-hand side.
std::vector<ValueType> b(submatrix.getRowCount());
storm::utility::vector::selectVectorValues(b, maybeStates,
transitionMatrix.getRowGroupIndices(),
stateActionRewardVector);
// Solve the corresponding system of equations.
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> solver = minMaxLinearEquationSolverFactory.create(
submatrix);
solver->solveEquations(dir, x, b);
// Set values of resulting vector according to previous result and return the result.
storm::utility::vector::setVectorValues<ValueType>(result, maybeStates, x);
}
storm::utility::vector::setVectorValues(result, goalStates, storm::utility::zero<ValueType>());
storm::utility::vector::setVectorValues(result, infinityStates, storm::utility::infinity<ValueType>());
return result;
storm::models::sparse::StandardRewardModel<ValueType> rewardModel(boost::none, std::move(rewardValues));
return SparseMdpPrctlHelper<ValueType>::computeReachabilityRewards(dir, transitionMatrix, backwardTransitions, rewardModel, psiStates, false, false, minMaxLinearEquationSolverFactory).values;
}
template<typename ValueType>
@ -550,8 +457,6 @@ namespace storm {
template double SparseMarkovAutomatonCslHelper::computeLraForMaximalEndComponent(OptimizationDirection dir, storm::storage::SparseMatrix<double> const& transitionMatrix, std::vector<double> const& exitRateVector, storm::storage::BitVector const& markovianStates, storm::storage::BitVector const& goalStates, storm::storage::MaximalEndComponent const& mec);
template std::vector<double> SparseMarkovAutomatonCslHelper::computeExpectedRewards(OptimizationDirection dir, storm::storage::SparseMatrix<double> const& transitionMatrix, storm::storage::SparseMatrix<double> const& backwardTransitions, storm::storage::BitVector const& goalStates, std::vector<double> const& stateRewards, storm::solver::MinMaxLinearEquationSolverFactory<double> const& minMaxLinearEquationSolverFactory);
template std::vector<storm::RationalNumber> SparseMarkovAutomatonCslHelper::computeBoundedUntilProbabilities(OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, std::vector<storm::RationalNumber> const& exitRateVector, storm::storage::BitVector const& markovianStates, storm::storage::BitVector const& psiStates, std::pair<double, double> const& boundsPair, storm::solver::MinMaxLinearEquationSolverFactory<storm::RationalNumber> const& minMaxLinearEquationSolverFactory);
template std::vector<storm::RationalNumber> SparseMarkovAutomatonCslHelper::computeUntilProbabilities(OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, storm::storage::SparseMatrix<storm::RationalNumber> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, bool qualitative, storm::solver::MinMaxLinearEquationSolverFactory<storm::RationalNumber> const& minMaxLinearEquationSolverFactory);
@ -566,8 +471,6 @@ namespace storm {
template storm::RationalNumber SparseMarkovAutomatonCslHelper::computeLraForMaximalEndComponent(OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, std::vector<storm::RationalNumber> const& exitRateVector, storm::storage::BitVector const& markovianStates, storm::storage::BitVector const& goalStates, storm::storage::MaximalEndComponent const& mec);
template std::vector<storm::RationalNumber> SparseMarkovAutomatonCslHelper::computeExpectedRewards(OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, storm::storage::SparseMatrix<storm::RationalNumber> const& backwardTransitions, storm::storage::BitVector const& goalStates, std::vector<storm::RationalNumber> const& stateRewards, storm::solver::MinMaxLinearEquationSolverFactory<storm::RationalNumber> const& minMaxLinearEquationSolverFactory);
}
}
}

16
src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.h
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@ -55,22 +55,6 @@ namespace storm {
*/
template <typename ValueType>
static ValueType computeLraForMaximalEndComponent(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, std::vector<ValueType> const& exitRateVector, storm::storage::BitVector const& markovianStates, storm::storage::BitVector const& goalStates, storm::storage::MaximalEndComponent const& mec);
/*!
* Computes the expected reward that is gained from each state before entering any of the goal states.
*
* @param dir Indicates whether minimal or maximal rewards are to be computed.
* @param transitionMatrix The transition matrix of the underlying Markov automaton.
* @param backwardTransitions The reversed transition relation of the underlying Markov automaton.
* @param goalStates The goal states that define until which point rewards are gained.
* @param stateRewards A vector that defines the reward gained in each state. For probabilistic states,
* this is an instantaneous reward that is fully gained and for Markovian states the actually gained
* reward is dependent on the expected time to stay in the state, i.e. it is gouverned by the exit rate
* of the state.
* @return A vector that contains the expected reward for each state of the model.
*/
template <typename ValueType>
static std::vector<ValueType> computeExpectedRewards(OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& goalStates, std::vector<ValueType> const& stateRewards, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory);
};
}

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