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Fixed an issue when computing expected rewards of Markov Automata

tempestpy_adaptions
TimQu 8 years ago
parent
b1b6b4b8cf
commit
e2cfa54d5b
1 changed files with 22 additions and 14 deletions
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  1. 36
      src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.cpp

36
src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.cpp
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@ -200,7 +200,7 @@ 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());
std::vector<ValueType> stateRewardWeights(transitionMatrix.getRowGroupCount(), storm::utility::zero<ValueType>());
for (auto const markovianState : markovianStates) {
stateRewardWeights[markovianState] = storm::utility::one<ValueType>() / exitRateVector[markovianState];
}
@ -382,10 +382,10 @@ namespace storm {
uint_fast64_t numberOfStates = transitionMatrix.getRowGroupCount();
// First, we need to check which states have infinite expected time (by definition).
// First, we need to check which states have infinite expected reward (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,
// If we need to compute the minimum expected rewards, 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.
@ -406,8 +406,7 @@ namespace storm {
infinityStates = storm::utility::graph::performProbGreater0A(transitionMatrix,
transitionMatrix.getRowGroupIndices(),
backwardTransitions,
storm::storage::BitVector(
numberOfStates, true),
~goalStates,
unionOfNonGoalBSccs);
} else {
// Otherwise, we have no infinity states.
@ -432,8 +431,7 @@ namespace storm {
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),
~goalStates,
unionOfNonGoalMaximalEndComponents);
} else {
// Otherwise, we have no infinity states.
@ -447,17 +445,27 @@ namespace storm {
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,
storm::storage::SparseMatrix<ValueType> submatrix;
std::vector<ValueType> b;
if (infinityStates.empty()) {
submatrix = transitionMatrix.getSubmatrix(true, maybeStates, maybeStates);
b.resize(submatrix.getRowCount());
storm::utility::vector::selectVectorValues(b, maybeStates,
transitionMatrix.getRowGroupIndices(),
stateActionRewardVector);
} else {
// If there are infinity states, we also have to eliminate the choices that lead from a maybe state to an infinity state
storm::storage::BitVector selectedChoices = transitionMatrix.getRowFilter(maybeStates, ~infinityStates);
submatrix = transitionMatrix.getSubmatrix(false, selectedChoices, maybeStates);
b.resize(submatrix.getRowCount());
storm::utility::vector::selectVectorValues(b, selectedChoices, stateActionRewardVector);
}
// Initialize the solution vector
std::vector<ValueType> x(maybeStates.getNumberOfSetBits());
// Solve the corresponding system of equations.
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> solver = minMaxLinearEquationSolverFactory.create(
submatrix);

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