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226 lines
13 KiB
226 lines
13 KiB
#ifndef STORM_MODELS_SPARSE_MARKOVAUTOMATON_H_
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#define STORM_MODELS_SPARSE_MARKOVAUTOMATON_H_
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#include "src/models/sparse/NondeterministicModel.h"
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#include "src/models/sparse/Ctmc.h"
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#include "src/utility/OsDetection.h"
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namespace storm {
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namespace models {
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namespace sparse {
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/*!
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* This class represents a Markov automaton.
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*/
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template<class ValueType, typename RewardModelType = StandardRewardModel<ValueType>>
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class MarkovAutomaton : public NondeterministicModel<ValueType, RewardModelType> {
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public:
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/*!
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* Constructs a model from the given data.
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*
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* @param transitionMatrix The matrix representing the transitions in the model in terms of rates.
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* @param stateLabeling The labeling of the states.
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* @param markovianStates A bit vector indicating the Markovian states of the automaton.
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* @param exitRates A vector storing the exit rates of the states.
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* @param rewardModels A mapping of reward model names to reward models.
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* @param optionalChoiceLabeling A vector that represents the labels associated with the choices of each state.
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*/
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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 = std::unordered_map<std::string, RewardModelType>(),
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boost::optional<std::vector<LabelSet>> const& optionalChoiceLabeling = boost::optional<std::vector<LabelSet>>());
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/*!
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* Constructs a model from the given data.
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*
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* For hybrid states (i.e., states with Markovian and probabilistic transitions), it is assumed that the first
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* choice corresponds to the markovian transitions.
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*
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* @param rateMatrix The matrix representing the transitions in the model in terms of rates.
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* @param stateLabeling The labeling of the states.
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* @param markovianStates A bit vector indicating the Markovian states of the automaton (i.e., states with at least one markovian transition).
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* @param rewardModels A mapping of reward model names to reward models.
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* @param optionalChoiceLabeling A vector that represents the labels associated with the choices of each state.
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*/
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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 = std::unordered_map<std::string, RewardModelType>(),
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boost::optional<std::vector<LabelSet>> const& optionalChoiceLabeling = boost::optional<std::vector<LabelSet>>());
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/*!
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* Constructs a model from the given data.
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*
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* For hybrid states (i.e., states with Markovian and probabilistic transitions), it is assumed that the first
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* choice corresponds to the markovian transitions.
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*
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* @param rateMatrix The matrix representing the transitions in the model in terms of rates.
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* @param stateLabeling The labeling of the states.
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* @param markovianStates A bit vector indicating the Markovian states of the automaton (i.e., states with at least one markovian transition).
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* @param rewardModels A mapping of reward model names to reward models.
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* @param optionalChoiceLabeling A vector that represents the labels associated with the choices of each state.
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*/
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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 = std::unordered_map<std::string, RewardModelType>(),
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boost::optional<std::vector<LabelSet>>&& optionalChoiceLabeling = boost::optional<std::vector<LabelSet>>());
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/*!
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* Constructs a model by moving the given data.
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*
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* @param transitionMatrix The matrix representing the transitions in the model in terms of rates.
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* @param stateLabeling The labeling of the states.
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* @param markovianStates A bit vector indicating the Markovian states of the automaton.
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* @param exitRates A vector storing the exit rates of the states.
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* @param rewardModels A mapping of reward model names to reward models.
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* @param optionalChoiceLabeling A vector that represents the labels associated with the choices of each state.
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*/
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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 = std::unordered_map<std::string, RewardModelType>(),
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boost::optional<std::vector<LabelSet>>&& optionalChoiceLabeling = boost::optional<std::vector<LabelSet>>());
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/*!
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* Constructs a model by moving the given data.
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*
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* @param transitionMatrix The matrix representing the transitions in the model in terms of rates.
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* @param stateLabeling The labeling of the states.
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* @param markovianStates A bit vector indicating the Markovian states of the automaton.
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* @param exitRates A vector storing the exit rates of the states.
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* @param probabilities Flag if transitions matrix contains probabilities or rates
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* @param rewardModels A mapping of reward model names to reward models.
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* @param optionalChoiceLabeling A vector that represents the labels associated with the choices of each state.
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*/
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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 = std::unordered_map<std::string, RewardModelType>(),
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boost::optional<std::vector<LabelSet>>&& optionalChoiceLabeling = boost::optional<std::vector<LabelSet>>());
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MarkovAutomaton(MarkovAutomaton<ValueType, RewardModelType> const& other) = default;
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MarkovAutomaton& operator=(MarkovAutomaton<ValueType, RewardModelType> const& other) = default;
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#ifndef WINDOWS
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MarkovAutomaton(MarkovAutomaton<ValueType, RewardModelType>&& other) = default;
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MarkovAutomaton& operator=(MarkovAutomaton<ValueType, RewardModelType>&& other) = default;
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#endif
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/*!
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* Retrieves whether the Markov automaton is closed.
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*
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* @return True iff the Markov automaton is closed.
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*/
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bool isClosed() const;
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/*!
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* Retrieves whether the given state is a hybrid state, i.e. Markovian and probabilistic.
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*
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* @param state The state for which determine whether it's hybrid.
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* @return True iff the state is hybrid.
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*/
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bool isHybridState(storm::storage::sparse::state_type state) const;
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/*!
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* Retrieves whether the given state is a Markovian state.
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*
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* @param state The state for which determine whether it's Markovian.
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* @return True iff the state is Markovian.
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*/
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bool isMarkovianState(storm::storage::sparse::state_type state) const;
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/*!
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* Retrieves whether the given state is a probabilistic state.
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*
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* @param state The state for which determine whether it's probabilistic.
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* @return True iff the state is probabilistic.
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*/
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bool isProbabilisticState(storm::storage::sparse::state_type state) const;
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/*!
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* Retrieves the vector representing the exit rates of the states.
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*
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* @return The exit rate vector of the model.
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*/
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std::vector<ValueType> const& getExitRates() const;
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/*!
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* Retrieves the vector representing the exit rates of the states.
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*
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* @return The exit rate vector of the model.
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*/
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std::vector<ValueType>& getExitRates();
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/*!
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* Retrieves the exit rate of the given state.
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*
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* @param state The state for which retrieve the exit rate.
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* @return The exit rate of the state.
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*/
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ValueType const& getExitRate(storm::storage::sparse::state_type state) const;
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/*!
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* Retrieves the maximal exit rate over all states of the model.
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*
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* @return The maximal exit rate of any state of the model.
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*/
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ValueType getMaximalExitRate() const;
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/*!
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* Retrieves the set of Markovian states of the model.
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*
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* @return A bit vector representing the Markovian states of the model.
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*/
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storm::storage::BitVector const& getMarkovianStates() const;
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/*!
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* Closes the Markov automaton. That is, this applies the maximal progress assumption to all hybrid states.
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*/
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void close();
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bool hasOnlyTrivialNondeterminism() const;
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std::shared_ptr<storm::models::sparse::Ctmc<ValueType, RewardModelType>> convertToCTMC();
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virtual void writeDotToStream(std::ostream& outStream, bool includeLabeling = true, storm::storage::BitVector const* subsystem = nullptr, std::vector<ValueType> const* firstValue = nullptr, std::vector<ValueType> const* secondValue = nullptr, std::vector<uint_fast64_t> const* stateColoring = nullptr, std::vector<std::string> const* colors = nullptr, std::vector<uint_fast64_t>* scheduler = nullptr, bool finalizeOutput = true) const override;
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std::size_t getSizeInBytes() const override;
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virtual void printModelInformationToStream(std::ostream& out) const override;
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private:
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/*!
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* Under the assumption that the Markovian choices of this Markov automaton are expressed in terms of
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* rates in the transition matrix, this procedure turns the rates into the corresponding probabilities by
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* dividing each entry by the sum of the rates for that choice.
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* Also sets the exitRates accordingly and throws an exception if the values for a non-markovian choice do not sum up to one.
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*/
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void turnRatesToProbabilities();
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/*!
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* Checks whether the automaton is closed by actually looking at the transition information.
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*/
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bool checkIsClosed() const;
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// A bit vector representing the set of Markovian states.
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storm::storage::BitVector markovianStates;
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// A vector storing the exit rates of all states of the model.
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std::vector<ValueType> exitRates;
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// A flag indicating whether the Markov automaton has been closed, which is typically a prerequisite
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// for model checking.
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bool closed;
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};
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} // namespace sparse
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} // namespace models
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} // namespace storm
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#endif /* STORM_MODELS_SPARSE_MARKOVAUTOMATON_H_ */
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