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