362 lines
24 KiB
362 lines
24 KiB
#include "storm/models/sparse/MarkovAutomaton.h"
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#include "storm/adapters/CarlAdapter.h"
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#include "storm/models/sparse/StandardRewardModel.h"
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#include "storm/solver/stateelimination/StateEliminator.h"
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#include "storm/storage/FlexibleSparseMatrix.h"
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#include "storm/utility/constants.h"
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#include "storm/utility/vector.h"
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#include "storm/utility/macros.h"
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#include "storm/exceptions/InvalidArgumentException.h"
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namespace storm {
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namespace models {
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namespace sparse {
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template <typename ValueType, typename RewardModelType>
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MarkovAutomaton<ValueType, RewardModelType>::MarkovAutomaton(storm::storage::SparseMatrix<ValueType> const& transitionMatrix,
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storm::models::sparse::StateLabeling const& stateLabeling,
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storm::storage::BitVector const& markovianStates,
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std::vector<ValueType> const& exitRates,
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std::unordered_map<std::string, RewardModelType> const& rewardModels,
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boost::optional<std::vector<LabelSet>> const& optionalChoiceLabeling)
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: NondeterministicModel<ValueType, RewardModelType>(storm::models::ModelType::MarkovAutomaton, transitionMatrix, stateLabeling, rewardModels, optionalChoiceLabeling), markovianStates(markovianStates), exitRates(exitRates), closed(this->checkIsClosed()) {
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this->turnRatesToProbabilities();
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}
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template <typename ValueType, typename RewardModelType>
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MarkovAutomaton<ValueType, RewardModelType>::MarkovAutomaton(storm::storage::SparseMatrix<ValueType>&& transitionMatrix,
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storm::models::sparse::StateLabeling&& stateLabeling,
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storm::storage::BitVector const& markovianStates,
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std::vector<ValueType> const& exitRates,
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std::unordered_map<std::string, RewardModelType>&& rewardModels,
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boost::optional<std::vector<LabelSet>>&& optionalChoiceLabeling)
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: NondeterministicModel<ValueType, RewardModelType>(storm::models::ModelType::MarkovAutomaton, std::move(transitionMatrix), std::move(stateLabeling), std::move(rewardModels), std::move(optionalChoiceLabeling)), markovianStates(markovianStates), exitRates(std::move(exitRates)), closed(this->checkIsClosed()) {
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this->turnRatesToProbabilities();
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}
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template <typename ValueType, typename RewardModelType>
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MarkovAutomaton<ValueType, RewardModelType>::MarkovAutomaton(storm::storage::SparseMatrix<ValueType> const& rateMatrix,
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storm::models::sparse::StateLabeling const& stateLabeling,
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storm::storage::BitVector const& markovianStates,
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std::unordered_map<std::string, RewardModelType> const& rewardModels,
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boost::optional<std::vector<LabelSet>> const& optionalChoiceLabeling)
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: NondeterministicModel<ValueType, RewardModelType>(storm::models::ModelType::MarkovAutomaton, rateMatrix, stateLabeling, rewardModels, optionalChoiceLabeling), markovianStates(markovianStates) {
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turnRatesToProbabilities();
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this->closed = checkIsClosed();
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}
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template <typename ValueType, typename RewardModelType>
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MarkovAutomaton<ValueType, RewardModelType>::MarkovAutomaton(storm::storage::SparseMatrix<ValueType>&& rateMatrix,
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storm::models::sparse::StateLabeling&& stateLabeling,
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storm::storage::BitVector&& markovianStates,
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std::unordered_map<std::string, RewardModelType>&& rewardModels,
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boost::optional<std::vector<LabelSet>>&& optionalChoiceLabeling)
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: NondeterministicModel<ValueType, RewardModelType>(storm::models::ModelType::MarkovAutomaton, rateMatrix, stateLabeling, rewardModels, optionalChoiceLabeling), markovianStates(markovianStates) {
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turnRatesToProbabilities();
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this->closed = checkIsClosed();
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}
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template <typename ValueType, typename RewardModelType>
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MarkovAutomaton<ValueType, RewardModelType>::MarkovAutomaton(storm::storage::SparseMatrix<ValueType>&& transitionMatrix,
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storm::models::sparse::StateLabeling&& stateLabeling,
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storm::storage::BitVector const& markovianStates,
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std::vector<ValueType> const& exitRates,
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bool probabilities,
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std::unordered_map<std::string, RewardModelType>&& rewardModels,
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boost::optional<std::vector<LabelSet>>&& optionalChoiceLabeling)
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: NondeterministicModel<ValueType, RewardModelType>(storm::models::ModelType::MarkovAutomaton, std::move(transitionMatrix), std::move(stateLabeling), std::move(rewardModels), std::move(optionalChoiceLabeling)), markovianStates(markovianStates), exitRates(std::move(exitRates)), closed(this->checkIsClosed()) {
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STORM_LOG_ASSERT(probabilities, "Matrix must be probabilistic.");
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STORM_LOG_ASSERT(this->getTransitionMatrix().isProbabilistic(), "Matrix must be probabilistic.");
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}
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template <typename ValueType, typename RewardModelType>
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bool MarkovAutomaton<ValueType, RewardModelType>::isClosed() const {
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return closed;
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}
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template <typename ValueType, typename RewardModelType>
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bool MarkovAutomaton<ValueType, RewardModelType>::isHybridState(storm::storage::sparse::state_type state) const {
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return isMarkovianState(state) && (this->getTransitionMatrix().getRowGroupSize(state) > 1);
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}
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template <typename ValueType, typename RewardModelType>
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bool MarkovAutomaton<ValueType, RewardModelType>::isMarkovianState(storm::storage::sparse::state_type state) const {
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return this->markovianStates.get(state);
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}
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template <typename ValueType, typename RewardModelType>
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bool MarkovAutomaton<ValueType, RewardModelType>::isProbabilisticState(storm::storage::sparse::state_type state) const {
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return !this->markovianStates.get(state);
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}
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template <typename ValueType, typename RewardModelType>
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std::vector<ValueType> const& MarkovAutomaton<ValueType, RewardModelType>::getExitRates() const {
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return this->exitRates;
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}
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template <typename ValueType, typename RewardModelType>
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std::vector<ValueType>& MarkovAutomaton<ValueType, RewardModelType>::getExitRates() {
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return this->exitRates;
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}
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template <typename ValueType, typename RewardModelType>
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ValueType const& MarkovAutomaton<ValueType, RewardModelType>::getExitRate(storm::storage::sparse::state_type state) const {
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return this->exitRates[state];
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}
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template <typename ValueType, typename RewardModelType>
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ValueType MarkovAutomaton<ValueType, RewardModelType>::getMaximalExitRate() const {
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ValueType result = storm::utility::zero<ValueType>();
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for (auto markovianState : this->markovianStates) {
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result = std::max(result, this->exitRates[markovianState]);
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}
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return result;
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}
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template <typename ValueType, typename RewardModelType>
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storm::storage::BitVector const& MarkovAutomaton<ValueType, RewardModelType>::getMarkovianStates() const {
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return this->markovianStates;
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}
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template <typename ValueType, typename RewardModelType>
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void MarkovAutomaton<ValueType, RewardModelType>::close() {
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if (!closed) {
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// Get the choices that we will keep
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storm::storage::BitVector keptChoices(this->getNumberOfChoices(), true);
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for(auto state : this->getMarkovianStates()) {
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if(this->getTransitionMatrix().getRowGroupSize(state) > 1) {
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// The state is hybrid, hence, we remove the first choice.
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keptChoices.set(this->getTransitionMatrix().getRowGroupIndices()[state], false);
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// Afterwards, the state will no longer be Markovian.
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this->markovianStates.set(state, false);
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exitRates[state] = storm::utility::zero<ValueType>();
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}
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}
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// Remove the Markovian choices for the different model ingredients
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this->getTransitionMatrix() = this->getTransitionMatrix().restrictRows(keptChoices);
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for(auto& rewModel : this->getRewardModels()) {
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if(rewModel.second.hasStateActionRewards()) {
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rewModel.second.getStateActionRewardVector() = storm::utility::vector::filterVector(rewModel.second.getStateActionRewardVector(), keptChoices);
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}
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if(rewModel.second.hasTransitionRewards()) {
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rewModel.second.getTransitionRewardMatrix() = rewModel.second.getTransitionRewardMatrix().restrictRows(keptChoices);
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}
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}
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if(this->hasChoiceLabeling()) {
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this->getOptionalChoiceLabeling() = storm::utility::vector::filterVector(this->getOptionalChoiceLabeling().get(), keptChoices);
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}
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// Mark the automaton as closed.
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closed = true;
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}
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}
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template <typename ValueType, typename RewardModelType>
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void MarkovAutomaton<ValueType, RewardModelType>::writeDotToStream(std::ostream& outStream, bool includeLabeling, storm::storage::BitVector const* subsystem, std::vector<ValueType> const* firstValue, std::vector<ValueType> const* secondValue, std::vector<uint_fast64_t> const* stateColoring, std::vector<std::string> const* colors, std::vector<uint_fast64_t>* scheduler, bool finalizeOutput) const {
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Model<ValueType, RewardModelType>::writeDotToStream(outStream, includeLabeling, subsystem, firstValue, secondValue, stateColoring, colors, scheduler, false);
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// Write the probability distributions for all the states.
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for (uint_fast64_t state = 0; state < this->getNumberOfStates(); ++state) {
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uint_fast64_t rowCount = this->getTransitionMatrix().getRowGroupIndices()[state + 1] - this->getTransitionMatrix().getRowGroupIndices()[state];
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bool highlightChoice = true;
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// For this, we need to iterate over all available nondeterministic choices in the current state.
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for (uint_fast64_t choice = 0; choice < rowCount; ++choice) {
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uint_fast64_t rowIndex = this->getTransitionMatrix().getRowGroupIndices()[state] + choice;
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typename storm::storage::SparseMatrix<ValueType>::const_rows row = this->getTransitionMatrix().getRow(rowIndex);
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if (scheduler != nullptr) {
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// If the scheduler picked the current choice, we will not make it dotted, but highlight it.
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if ((*scheduler)[state] == choice) {
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highlightChoice = true;
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} else {
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highlightChoice = false;
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}
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}
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// If it's not a Markovian state or the current row is the first choice for this state, then we
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// are dealing with a probabilitic choice.
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if (!markovianStates.get(state) || choice != 0) {
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// For each nondeterministic choice, we draw an arrow to an intermediate node to better display
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// the grouping of transitions.
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outStream << "\t\"" << state << "c" << choice << "\" [shape = \"point\"";
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// If we were given a scheduler to highlight, we do so now.
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if (scheduler != nullptr) {
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if (highlightChoice) {
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outStream << ", fillcolor=\"red\"";
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}
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}
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outStream << "];" << std::endl;
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outStream << "\t" << state << " -> \"" << state << "c" << choice << "\" [ label= \"" << rowIndex << "\"";
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// If we were given a scheduler to highlight, we do so now.
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if (scheduler != nullptr) {
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if (highlightChoice) {
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outStream << ", color=\"red\", penwidth = 2";
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} else {
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outStream << ", style = \"dotted\"";
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}
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}
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outStream << "];" << std::endl;
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// Now draw all probabilitic arcs that belong to this nondeterminstic choice.
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for (auto const& transition : row) {
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if (subsystem == nullptr || subsystem->get(transition.getColumn())) {
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outStream << "\t\"" << state << "c" << choice << "\" -> " << transition.getColumn() << " [ label= \"" << transition.getValue() << "\" ]";
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// If we were given a scheduler to highlight, we do so now.
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if (scheduler != nullptr) {
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if (highlightChoice) {
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outStream << " [color=\"red\", penwidth = 2]";
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} else {
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outStream << " [style = \"dotted\"]";
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}
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}
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outStream << ";" << std::endl;
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}
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}
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} else {
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// In this case we are emitting a Markovian choice, so draw the arrows directly to the target states.
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for (auto const& transition : row) {
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if (subsystem == nullptr || subsystem->get(transition.getColumn())) {
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outStream << "\t\"" << state << "\" -> " << transition.getColumn() << " [ label= \"" << transition.getValue() << " (" << this->exitRates[state] << ")\" ]";
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}
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}
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}
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}
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}
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if (finalizeOutput) {
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outStream << "}" << std::endl;
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}
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}
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template <typename ValueType, typename RewardModelType>
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void MarkovAutomaton<ValueType, RewardModelType>::turnRatesToProbabilities() {
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this->exitRates.resize(this->getNumberOfStates());
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for (uint_fast64_t state = 0; state< this->getNumberOfStates(); ++state) {
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uint_fast64_t row = this->getTransitionMatrix().getRowGroupIndices()[state];
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if (this->markovianStates.get(state)) {
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this->exitRates[state] = this->getTransitionMatrix().getRowSum(row);
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for (auto& transition : this->getTransitionMatrix().getRow(row)) {
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transition.setValue(transition.getValue() / this->exitRates[state]);
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}
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++row;
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}
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for (; row < this->getTransitionMatrix().getRowGroupIndices()[state+1]; ++row) {
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STORM_LOG_THROW(storm::utility::isOne(this->getTransitionMatrix().getRowSum(row)), storm::exceptions::InvalidArgumentException, "Entries of transition matrix do not sum up to one for (non-Markovian) choice " << row << " of state " << state << " (sum is " << this->getTransitionMatrix().getRowSum(row) << ").");
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}
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}
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}
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template <typename ValueType, typename RewardModelType>
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bool MarkovAutomaton<ValueType, RewardModelType>::hasOnlyTrivialNondeterminism() const {
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// Check every state
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for (uint_fast64_t state = 0; state < this->getNumberOfStates(); ++state) {
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// Get number of choices in current state
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uint_fast64_t numberChoices = this->getTransitionMatrix().getRowGroupIndices()[state + 1] - this->getTransitionMatrix().getRowGroupIndices()[state];
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if (isMarkovianState(state)) {
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STORM_LOG_ASSERT(numberChoices == 1, "Wrong number of choices for markovian state.");
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}
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if (numberChoices > 1) {
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STORM_LOG_ASSERT(isProbabilisticState(state), "State is not probabilistic.");
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return false;
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}
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}
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return true;
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}
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template <typename ValueType, typename RewardModelType>
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bool MarkovAutomaton<ValueType, RewardModelType>::checkIsClosed() const {
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for (auto state : markovianStates) {
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if (this->getTransitionMatrix().getRowGroupSize(state) > 1) {
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return false;
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}
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}
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return true;
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}
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template <typename ValueType, typename RewardModelType>
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std::shared_ptr<storm::models::sparse::Ctmc<ValueType, RewardModelType>> MarkovAutomaton<ValueType, RewardModelType>::convertToCTMC() const {
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STORM_LOG_TRACE("MA matrix:" << std::endl << this->getTransitionMatrix());
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STORM_LOG_TRACE("Markovian states: " << getMarkovianStates());
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// Eliminate all probabilistic states by state elimination
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// Initialize
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storm::storage::FlexibleSparseMatrix<ValueType> flexibleMatrix(this->getTransitionMatrix());
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storm::storage::FlexibleSparseMatrix<ValueType> flexibleBackwardTransitions(this->getTransitionMatrix().transpose());
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storm::solver::stateelimination::StateEliminator<ValueType> stateEliminator(flexibleMatrix, flexibleBackwardTransitions);
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for (uint_fast64_t state = 0; state < this->getNumberOfStates(); ++state) {
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STORM_LOG_ASSERT(!this->isHybridState(state), "State is hybrid.");
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if (this->isProbabilisticState(state)) {
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// Eliminate this probabilistic state
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stateEliminator.eliminateState(state, true);
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STORM_LOG_TRACE("Flexible matrix after eliminating state " << state << ":" << std::endl << flexibleMatrix);
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}
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}
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// Create the rate matrix for the CTMC
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storm::storage::SparseMatrixBuilder<ValueType> transitionMatrixBuilder(0, 0, 0, false, false);
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// Remember state to keep
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storm::storage::BitVector keepStates(this->getNumberOfStates(), true);
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for (uint_fast64_t state = 0; state < this->getNumberOfStates(); ++state) {
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if (storm::utility::isZero(flexibleMatrix.getRowSum(state))) {
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// State is eliminated and can be discarded
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keepStates.set(state, false);
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} else {
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STORM_LOG_ASSERT(this->isMarkovianState(state), "State is not markovian.");
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// Copy transitions
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for (uint_fast64_t row = flexibleMatrix.getRowGroupIndices()[state]; row < flexibleMatrix.getRowGroupIndices()[state + 1]; ++row) {
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for (auto const& entry : flexibleMatrix.getRow(row)) {
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// Convert probabilities into rates
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transitionMatrixBuilder.addNextValue(state, entry.getColumn(), entry.getValue() * exitRates[state]);
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}
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}
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}
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}
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storm::storage::SparseMatrix<ValueType> rateMatrix = transitionMatrixBuilder.build();
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rateMatrix = rateMatrix.getSubmatrix(false, keepStates, keepStates, false);
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STORM_LOG_TRACE("New CTMC matrix:" << std::endl << rateMatrix);
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// Construct CTMC
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storm::models::sparse::StateLabeling stateLabeling = this->getStateLabeling().getSubLabeling(keepStates);
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boost::optional<std::vector<LabelSet>> optionalChoiceLabeling = this->getOptionalChoiceLabeling();
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if (optionalChoiceLabeling) {
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optionalChoiceLabeling = storm::utility::vector::filterVector(optionalChoiceLabeling.get(), keepStates);
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}
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//TODO update reward models according to kept states
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STORM_LOG_WARN_COND(this->getRewardModels().empty(), "Conversion of MA to CTMC does not preserve rewards.");
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std::unordered_map<std::string, RewardModelType> rewardModels = this->getRewardModels();
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return std::make_shared<storm::models::sparse::Ctmc<ValueType, RewardModelType>>(std::move(rateMatrix), std::move(stateLabeling), std::move(rewardModels), std::move(optionalChoiceLabeling));
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}
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template<typename ValueType, typename RewardModelType>
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void MarkovAutomaton<ValueType, RewardModelType>::printModelInformationToStream(std::ostream& out) const {
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this->printModelInformationHeaderToStream(out);
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out << "Choices: \t" << this->getNumberOfChoices() << std::endl;
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out << "Markovian St.: \t" << this->getMarkovianStates().getNumberOfSetBits() << std::endl;
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out << "Max. Rate.: \t" << this->getMaximalExitRate() << std::endl;
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this->printModelInformationFooterToStream(out);
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}
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template class MarkovAutomaton<double>;
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// template class MarkovAutomaton<float>;
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#ifdef STORM_HAVE_CARL
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template class MarkovAutomaton<storm::RationalNumber>;
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template class MarkovAutomaton<double, storm::models::sparse::StandardRewardModel<storm::Interval>>;
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template class MarkovAutomaton<storm::RationalFunction>;
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#endif
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} // namespace sparse
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} // namespace models
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} // namespace storm
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