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Merge branch 'master' into multi-objective

tempestpy_adaptions
TimQu 8 years ago
parent
2da827b216 0bb1c5855e
commit
64c5a313d2
4 changed files with 93 additions and 80 deletions
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  1. 97
      src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.cpp
  2. 9
      src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.h
  3. 40
      src/storm/models/sparse/StandardRewardModel.cpp
  4. 27
      src/storm/models/sparse/StandardRewardModel.h

97
src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.cpp
View File

@ -190,8 +190,12 @@ namespace storm {
template <typename ValueType>
template <typename RewardModelType>
std::vector<ValueType> SparseMarkovAutomatonCslHelper<ValueType>::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> totalRewardVector = rewardModel.getTotalRewardVector(transitionMatrix.getRowCount(), transitionMatrix, storm::storage::BitVector(transitionMatrix.getRowGroupCount(), true));
return computeExpectedRewards(dir, transitionMatrix, backwardTransitions, exitRateVector, markovianStates, psiStates, totalRewardVector, minMaxLinearEquationSolverFactory);
std::vector<ValueType> stateRewardWeights(transitionMatrix.getRowGroupCount());
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);
}
template<typename ValueType>
@ -351,58 +355,77 @@ namespace storm {
template <typename ValueType>
std::vector<ValueType> SparseMarkovAutomatonCslHelper<ValueType>::computeTimes(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) {
uint_fast64_t numberOfStates = transitionMatrix.getRowGroupCount();
std::vector<ValueType> rewardValues(numberOfStates, storm::utility::zero<ValueType>());
storm::utility::vector::setVectorValues(rewardValues, markovianStates, storm::utility::one<ValueType>());
return computeExpectedRewards(dir, transitionMatrix, backwardTransitions, exitRateVector, markovianStates, psiStates, rewardValues, minMaxLinearEquationSolverFactory);
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<ValueType>::computeExpectedRewards(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& goalStates, std::vector<ValueType> const& stateRewards, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory) {
template<typename ValueType>
std::vector<ValueType>
SparseMarkovAutomatonCslHelper<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 &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 (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<double> sccDecomposition(transitionMatrix, ~goalStates, true, true);
storm::storage::StronglyConnectedComponentDecomposition<double> 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 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);
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<double> mecDecomposition(transitionMatrix, backwardTransitions, ~goalStates);
storm::storage::MaximalEndComponentDecomposition<double> 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) {
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);
infinityStates = storm::utility::graph::performProbGreater0E(backwardTransitions,
storm::storage::BitVector(
numberOfStates, true),
unionOfNonGoalMaximalEndComponents);
} else {
// Otherwise, we have no infinity states.
infinityStates = storm::storage::BitVector(numberOfStates);
@ -413,33 +436,31 @@ namespace storm {
// 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);
// Now prepare the expected reward values for all states so they can be used as the right-hand side of the equation system.
std::vector<ValueType> rewardValues(stateRewards);
for (auto state : markovianStates) {
rewardValues[state] = rewardValues[state] / exitRateVector[state];
}
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::selectVectorValuesRepeatedly(b, maybeStates, transitionMatrix.getRowGroupIndices(), rewardValues);
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);
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;
}

9
src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.h
View File

@ -32,7 +32,7 @@ namespace storm {
/*!
* Computes the long-run average value for the given maximal end component of a Markov automaton.
*
* @param minimize Sets whether the long-run average is to be minimized or maximized.
* @param dir Sets whether the long-run average is to be minimized or maximized.
* @param transitionMatrix The transition matrix of the underlying Markov automaton.
* @param markovianStates A bit vector storing all markovian states.
* @param exitRateVector A vector with exit rates for all states. Exit rates of probabilistic states are
@ -46,12 +46,9 @@ namespace storm {
/*!
* Computes the expected reward that is gained from each state before entering any of the goal states.
*
* @param minimize Indicates whether minimal or maximal rewards are to be computed.
* @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 exitRateVector A vector with exit rates for all states. Exit rates of probabilistic states are
* assumed to be zero.
* @param markovianStates A bit vector storing all markovian states.
* @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
@ -59,7 +56,7 @@ namespace storm {
* of the state.
* @return A vector that contains the expected reward for each state of the model.
*/
static std::vector<ValueType> computeExpectedRewards(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& goalStates, std::vector<ValueType> const& stateRewards, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory);
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);
};
}

40
src/storm/models/sparse/StandardRewardModel.cpp
View File

@ -161,7 +161,7 @@ namespace storm {
this->optionalStateActionRewardVector = boost::none;
}
}
template<typename ValueType>
template<typename MatrixValueType>
std::vector<ValueType> StandardRewardModel<ValueType>::getTotalRewardVector(storm::storage::SparseMatrix<MatrixValueType> const& transitionMatrix) const {
@ -211,24 +211,23 @@ namespace storm {
}
return result;
}
template<typename ValueType>
std::vector<ValueType> StandardRewardModel<ValueType>::getTotalStateActionRewardVector(uint_fast64_t numberOfRows, std::vector<uint_fast64_t> const& rowGroupIndices) const {
std::vector<ValueType> result = this->hasStateActionRewards() ? this->getStateActionRewardVector() : std::vector<ValueType>(numberOfRows);
if (this->hasStateRewards()) {
storm::utility::vector::addVectorToGroupedVector(result, this->getStateRewardVector(), rowGroupIndices);
}
return result;
}
template<typename ValueType>
std::vector<ValueType> StandardRewardModel<ValueType>::getTotalStateActionRewardVector(uint_fast64_t numberOfRows, std::vector<uint_fast64_t> const& rowGroupIndices, storm::storage::BitVector const& filter) const {
std::vector<ValueType> result(numberOfRows);
if (this->hasStateRewards()) {
storm::utility::vector::selectVectorValuesRepeatedly(result, filter, rowGroupIndices, this->getStateRewardVector());
template<typename MatrixValueType>
std::vector<ValueType> StandardRewardModel<ValueType>::getTotalActionRewardVector(storm::storage::SparseMatrix<MatrixValueType> const& transitionMatrix, std::vector<MatrixValueType> const& stateRewardWeights) const {
std::vector<ValueType> result;
if (this->hasTransitionRewards()) {
result = transitionMatrix.getPointwiseProductRowSumVector(this->getTransitionRewardMatrix());
} else {
result = std::vector<ValueType>(transitionMatrix.getRowCount());
}
if (this->hasStateActionRewards()) {
storm::utility::vector::addFilteredVectorGroupsToGroupedVector(result, this->getStateActionRewardVector(), filter, rowGroupIndices);
storm::utility::vector::addVectors(result, this->getStateActionRewardVector(), result);
}
if (this->hasStateRewards()) {
std::vector<ValueType> scaledStateRewardVector(transitionMatrix.getRowGroupCount());
storm::utility::vector::multiplyVectorsPointwise(this->getStateRewardVector(), stateRewardWeights, scaledStateRewardVector);
storm::utility::vector::addVectorToGroupedVector(result, scaledStateRewardVector, transitionMatrix.getRowGroupIndices());
}
return result;
}
@ -304,6 +303,7 @@ namespace storm {
template std::vector<double> StandardRewardModel<double>::getTotalRewardVector(storm::storage::SparseMatrix<double> const& transitionMatrix) const;
template std::vector<double> StandardRewardModel<double>::getTotalRewardVector(uint_fast64_t numberOfRows, storm::storage::SparseMatrix<double> const& transitionMatrix, storm::storage::BitVector const& filter) const;
template std::vector<double> StandardRewardModel<double>::getTotalRewardVector(storm::storage::SparseMatrix<double> const& transitionMatrix, std::vector<double> const& weights, bool scaleTransAndActions) const;
template std::vector<double> StandardRewardModel<double>::getTotalActionRewardVector(storm::storage::SparseMatrix<double> const& transitionMatrix, std::vector<double> const& stateRewardWeights) const;
template void StandardRewardModel<double>::reduceToStateBasedRewards(storm::storage::SparseMatrix<double> const& transitionMatrix, bool reduceToStateRewards);
template void StandardRewardModel<double>::setStateActionReward(uint_fast64_t choiceIndex, double const & newValue);
template void StandardRewardModel<double>::setStateReward(uint_fast64_t state, double const & newValue);
@ -313,6 +313,7 @@ namespace storm {
template std::vector<float> StandardRewardModel<float>::getTotalRewardVector(uint_fast64_t numberOfRows, storm::storage::SparseMatrix<float> const& transitionMatrix, storm::storage::BitVector const& filter) const;
template std::vector<float> StandardRewardModel<float>::getTotalRewardVector(storm::storage::SparseMatrix<float> const& transitionMatrix) const;
template std::vector<float> StandardRewardModel<float>::getTotalRewardVector(storm::storage::SparseMatrix<float> const& transitionMatrix, std::vector<float> const& weights, bool scaleTransAndActions) const;
template std::vector<float> StandardRewardModel<float>::getTotalActionRewardVector(storm::storage::SparseMatrix<float> const& transitionMatrix, std::vector<float> const& stateRewardWeights) const;
template void StandardRewardModel<float>::reduceToStateBasedRewards(storm::storage::SparseMatrix<float> const& transitionMatrix, bool reduceToStateRewards);
template void StandardRewardModel<float>::setStateActionReward(uint_fast64_t choiceIndex, float const & newValue);
template void StandardRewardModel<float>::setStateReward(uint_fast64_t state, float const & newValue);
@ -323,24 +324,27 @@ namespace storm {
template std::vector<storm::RationalNumber> StandardRewardModel<storm::RationalNumber>::getTotalRewardVector(uint_fast64_t numberOfRows, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, storm::storage::BitVector const& filter) const;
template std::vector<storm::RationalNumber> StandardRewardModel<storm::RationalNumber>::getTotalRewardVector(storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix) const;
template std::vector<storm::RationalNumber> StandardRewardModel<storm::RationalNumber>::getTotalRewardVector(storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, std::vector<storm::RationalNumber> const& weights, bool scaleTransAndActions) const;
template std::vector<storm::RationalNumber> StandardRewardModel<storm::RationalNumber>::getTotalActionRewardVector(storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, std::vector<storm::RationalNumber> const& stateRewardWeights) const;
template void StandardRewardModel<storm::RationalNumber>::reduceToStateBasedRewards(storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix, bool reduceToStateRewards);
template void StandardRewardModel<storm::RationalNumber>::setStateActionReward(uint_fast64_t choiceIndex, storm::RationalNumber const & newValue);
template void StandardRewardModel<storm::RationalNumber>::setStateReward(uint_fast64_t state, storm::RationalNumber const & newValue);
template class StandardRewardModel<storm::RationalNumber>;
template std::ostream& operator<<<storm::RationalNumber>(std::ostream& out, StandardRewardModel<storm::RationalNumber> const& rewardModel);
template std::vector<storm::RationalFunction> StandardRewardModel<storm::RationalFunction>::getTotalRewardVector(uint_fast64_t numberOfRows, storm::storage::SparseMatrix<storm::RationalFunction> const& transitionMatrix, storm::storage::BitVector const& filter) const;
template std::vector<storm::RationalFunction> StandardRewardModel<storm::RationalFunction>::getTotalRewardVector(storm::storage::SparseMatrix<storm::RationalFunction> const& transitionMatrix) const;
template std::vector<storm::RationalFunction> StandardRewardModel<storm::RationalFunction>::getTotalRewardVector(storm::storage::SparseMatrix<storm::RationalFunction> const& transitionMatrix, std::vector<storm::RationalFunction> const& weights, bool scaleTransAndActions) const;
template std::vector<storm::RationalFunction> StandardRewardModel<storm::RationalFunction>::getTotalActionRewardVector(storm::storage::SparseMatrix<storm::RationalFunction> const& transitionMatrix, std::vector<storm::RationalFunction> const& stateRewardWeights) const;
template void StandardRewardModel<storm::RationalFunction>::reduceToStateBasedRewards(storm::storage::SparseMatrix<storm::RationalFunction> const& transitionMatrix, bool reduceToStateRewards);
template void StandardRewardModel<storm::RationalFunction>::setStateActionReward(uint_fast64_t choiceIndex, storm::RationalFunction const & newValue);
template void StandardRewardModel<storm::RationalFunction>::setStateReward(uint_fast64_t state, storm::RationalFunction const & newValue);
template class StandardRewardModel<storm::RationalFunction>;
template std::ostream& operator<<<storm::RationalFunction>(std::ostream& out, StandardRewardModel<storm::RationalFunction> const& rewardModel);
template std::vector<storm::Interval> StandardRewardModel<storm::Interval>::getTotalRewardVector(uint_fast64_t numberOfRows, storm::storage::SparseMatrix<double> const& transitionMatrix, storm::storage::BitVector const& filter) const;
template std::vector<storm::Interval> StandardRewardModel<storm::Interval>::getTotalRewardVector(storm::storage::SparseMatrix<double> const& transitionMatrix) const;
template std::vector<storm::Interval> StandardRewardModel<storm::Interval>::getTotalRewardVector(storm::storage::SparseMatrix<double> const& transitionMatrix, std::vector<double> const& weights, bool scaleTransAndActions) const;
template std::vector<storm::Interval> StandardRewardModel<storm::Interval>::getTotalActionRewardVector(storm::storage::SparseMatrix<double> const& transitionMatrix, std::vector<double> const& stateRewardWeights) const;
template void StandardRewardModel<storm::Interval>::setStateActionReward(uint_fast64_t choiceIndex, double const & newValue);
template void StandardRewardModel<storm::Interval>::setStateActionReward(uint_fast64_t choiceIndex, storm::Interval const & newValue);
template void StandardRewardModel<storm::Interval>::setStateReward(uint_fast64_t state, double const & newValue);

27
src/storm/models/sparse/StandardRewardModel.h
View File

@ -217,28 +217,19 @@ namespace storm {
*/
template<typename MatrixValueType>
std::vector<ValueType> getTotalRewardVector(uint_fast64_t numberOfRows, storm::storage::SparseMatrix<MatrixValueType> const& transitionMatrix, storm::storage::BitVector const& filter) const;
/*!
* Creates a vector representing the complete state action reward vector based on the state-, state-action-
* and transition-based rewards in the reward model.
*
* @param numberOfRows The total number of rows of the resulting vector.
* @param rowGroupIndices The starting indices of the row groups.
* @return The full state-action reward vector.
*/
std::vector<ValueType> getTotalStateActionRewardVector(uint_fast64_t numberOfRows, std::vector<uint_fast64_t> const& rowGroupIndices) const;
/*!
* Creates a vector representing the complete state action reward vector based on the state- and
* state-action rewards in the reward model.
* Creates a vector representing the complete action-based rewards, i.e., state-action- and
* transition-based rewards
*
* @param numberOfRows The total number of rows of the resulting vector.
* @param rowGroupIndices The starting indices of the row groups.
* @param filter A bit vector indicating which row groups to select.
* @return The full state-action reward vector.
* @param transitionMatrix The matrix that is used to weight the values of the transition reward matrix.
* @return The state-action reward vector that considers state-action rewards and transition rewards of this reward model.
*/
std::vector<ValueType> getTotalStateActionRewardVector(uint_fast64_t numberOfRows, std::vector<uint_fast64_t> const& rowGroupIndices, storm::storage::BitVector const& filter) const;
template<typename MatrixValueType>
std::vector<ValueType> getTotalActionRewardVector(storm::storage::SparseMatrix<MatrixValueType> const& transitionMatrix, std::vector<MatrixValueType> const& stateRewardWeights) const;
/*!
* Sets the given value in the state-action reward vector at the given row. This assumes that the reward
* model has state-action rewards.

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