#include "src/builder/ExplicitPrismModelBuilder.h"
#include <map>
#include "src/models/sparse/Dtmc.h"
#include "src/models/sparse/Ctmc.h"
#include "src/models/sparse/Mdp.h"
#include "src/models/sparse/StandardRewardModel.h"
#include "src/storage/expressions/ExpressionManager.h"
#include "src/settings/modules/MarkovChainSettings.h"
#include "src/settings/modules/IOSettings.h"
#include "src/generator/PrismNextStateGenerator.h"
#include "src/generator/PrismStateLabelingGenerator.h"
#include "src/utility/prism.h"
#include "src/utility/constants.h"
#include "src/utility/macros.h"
#include "src/utility/ConstantsComparator.h"
#include "src/exceptions/WrongFormatException.h"
#include "src/exceptions/InvalidArgumentException.h"
#include "src/exceptions/InvalidOperationException.h"
namespace storm {
namespace builder {
/*!
* A structure that is used to keep track of a reward model currently being built.
*/
template <typename ValueType>
struct RewardModelBuilder {
public:
RewardModelBuilder(bool deterministicModel, bool hasStateRewards, bool hasStateActionRewards, bool hasTransitionRewards) : hasStateRewards(hasStateRewards), hasStateActionRewards(hasStateActionRewards), hasTransitionRewards(hasTransitionRewards), stateRewardVector(), stateActionRewardVector() {
// Intentionally left empty.
}
storm::models::sparse::StandardRewardModel<ValueType> build(uint_fast64_t rowCount, uint_fast64_t columnCount, uint_fast64_t rowGroupCount) {
boost::optional<std::vector<ValueType>> optionalStateRewardVector;
if (hasStateRewards) {
stateRewardVector.resize(rowGroupCount);
optionalStateRewardVector = std::move(stateRewardVector);
}
boost::optional<std::vector<ValueType>> optionalStateActionRewardVector;
if (hasStateActionRewards) {
stateActionRewardVector.resize(rowCount);
optionalStateActionRewardVector = std::move(stateActionRewardVector);
}
return storm::models::sparse::StandardRewardModel<ValueType>(std::move(optionalStateRewardVector), std::move(optionalStateActionRewardVector));
}
bool hasStateRewards;
bool hasStateActionRewards;
bool hasTransitionRewards;
// The state reward vector.
std::vector<ValueType> stateRewardVector;
// The state-action reward vector.
std::vector<ValueType> stateActionRewardVector;
};
template <typename ValueType, typename RewardModelType, typename StateType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::StateInformation::StateInformation(uint_fast64_t numberOfStates) : valuations(numberOfStates) {
// Intentionally left empty.
}
template <typename ValueType, typename RewardModelType, typename StateType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::InternalStateInformation::InternalStateInformation() : stateStorage(64, 10), initialStateIndices(), bitsPerState(64), numberOfStates() {
// Intentionally left empty.
}
template <typename ValueType, typename RewardModelType, typename StateType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::InternalStateInformation::InternalStateInformation(uint64_t bitsPerState) : stateStorage(bitsPerState, 10000000), initialStateIndices(), bitsPerState(bitsPerState), numberOfStates() {
// Intentionally left empty.
}
template <typename ValueType, typename RewardModelType, typename StateType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::ModelComponents::ModelComponents() : transitionMatrix(), stateLabeling(), rewardModels(), choiceLabeling() {
// Intentionally left empty.
}
template <typename ValueType, typename RewardModelType, typename StateType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::Options::Options() : explorationOrder(storm::settings::getModule<storm::settings::modules::IOSettings>().getExplorationOrder()), buildCommandLabels(false), buildAllRewardModels(true), buildStateInformation(false), rewardModelsToBuild(), constantDefinitions(), buildAllLabels(true), labelsToBuild(), expressionLabels(), terminalStates(), negatedTerminalStates() {
// Intentionally left empty.
}
template <typename ValueType, typename RewardModelType, typename StateType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::Options::Options(storm::logic::Formula const& formula) : explorationOrder(storm::settings::getModule<storm::settings::modules::IOSettings>().getExplorationOrder()), buildCommandLabels(false), buildAllRewardModels(false), buildStateInformation(false), rewardModelsToBuild(), constantDefinitions(), buildAllLabels(false), labelsToBuild(std::set<std::string>()), expressionLabels(std::vector<storm::expressions::Expression>()), terminalStates(), negatedTerminalStates() {
this->preserveFormula(formula);
}
template <typename ValueType, typename RewardModelType, typename StateType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::Options::Options(std::vector<std::shared_ptr<const storm::logic::Formula>> const& formulas) : explorationOrder(storm::settings::getModule<storm::settings::modules::IOSettings>().getExplorationOrder()), buildCommandLabels(false), buildAllRewardModels(false), buildStateInformation(false), rewardModelsToBuild(), constantDefinitions(), buildAllLabels(false), labelsToBuild(), expressionLabels(), terminalStates(), negatedTerminalStates() {
if (formulas.empty()) {
this->buildAllRewardModels = true;
this->buildAllLabels = true;
} else {
for (auto const& formula : formulas) {
this->preserveFormula(*formula);
}
if (formulas.size() == 1) {
this->setTerminalStatesFromFormula(*formulas.front());
}
}
}
template <typename ValueType, typename RewardModelType, typename StateType>
void ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::Options::setTerminalStatesFromFormula(storm::logic::Formula const& formula) {
if (formula.isAtomicExpressionFormula()) {
terminalStates = formula.asAtomicExpressionFormula().getExpression();
} else if (formula.isAtomicLabelFormula()) {
terminalStates = formula.asAtomicLabelFormula().getLabel();
} else if (formula.isEventuallyFormula()) {
storm::logic::Formula const& sub = formula.asEventuallyFormula().getSubformula();
if (sub.isAtomicExpressionFormula() || sub.isAtomicLabelFormula()) {
this->setTerminalStatesFromFormula(sub);
}
} else if (formula.isUntilFormula()) {
storm::logic::Formula const& right = formula.asUntilFormula().getRightSubformula();
if (right.isAtomicExpressionFormula() || right.isAtomicLabelFormula()) {
this->setTerminalStatesFromFormula(right);
}
storm::logic::Formula const& left = formula.asUntilFormula().getLeftSubformula();
if (left.isAtomicExpressionFormula()) {
negatedTerminalStates = left.asAtomicExpressionFormula().getExpression();
} else if (left.isAtomicLabelFormula()) {
negatedTerminalStates = left.asAtomicLabelFormula().getLabel();
}
} else if (formula.isProbabilityOperatorFormula()) {
storm::logic::Formula const& sub = formula.asProbabilityOperatorFormula().getSubformula();
if (sub.isEventuallyFormula() || sub.isUntilFormula()) {
this->setTerminalStatesFromFormula(sub);
}
}
}
template <typename ValueType, typename RewardModelType, typename StateType>
void ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::Options::preserveFormula(storm::logic::Formula const& formula) {
// If we already had terminal states, we need to erase them.
if (terminalStates) {
terminalStates.reset();
}
if (negatedTerminalStates) {
negatedTerminalStates.reset();
}
// If we are not required to build all reward models, we determine the reward models we need to build.
if (!buildAllRewardModels) {
std::set<std::string> referencedRewardModels = formula.getReferencedRewardModels();
rewardModelsToBuild.insert(referencedRewardModels.begin(), referencedRewardModels.end());
}
// Extract all the labels used in the formula.
if (!buildAllLabels) {
if (!labelsToBuild) {
labelsToBuild = std::set<std::string>();
}
std::vector<std::shared_ptr<storm::logic::AtomicLabelFormula const>> atomicLabelFormulas = formula.getAtomicLabelFormulas();
for (auto const& formula : atomicLabelFormulas) {
labelsToBuild.get().insert(formula.get()->getLabel());
}
}
// Extract all the expressions used in the formula.
std::vector<std::shared_ptr<storm::logic::AtomicExpressionFormula const>> atomicExpressionFormulas = formula.getAtomicExpressionFormulas();
for (auto const& formula : atomicExpressionFormulas) {
if (!expressionLabels) {
expressionLabels = std::vector<storm::expressions::Expression>();
}
expressionLabels.get().push_back(formula.get()->getExpression());
}
}
template <typename ValueType, typename RewardModelType, typename StateType>
void ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::Options::addConstantDefinitionsFromString(storm::prism::Program const& program, std::string const& constantDefinitionString) {
constantDefinitions = storm::utility::prism::parseConstantDefinitionString(program, constantDefinitionString);
}
template <typename ValueType, typename RewardModelType, typename StateType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::ExplicitPrismModelBuilder(storm::prism::Program const& program, Options const& options) : program(program), options(options) {
// Start by defining the undefined constants in the model.
if (options.constantDefinitions) {
this->program = program.defineUndefinedConstants(options.constantDefinitions.get());
} else {
this->program = program;
}
// If the program still contains undefined constants and we are not in a parametric setting, assemble an appropriate error message.
if (!std::is_same<ValueType, storm::RationalFunction>::value && this->program.hasUndefinedConstants()) {
std::vector<std::reference_wrapper<storm::prism::Constant const>> undefinedConstants = this->program.getUndefinedConstants();
std::stringstream stream;
bool printComma = false;
for (auto const& constant : undefinedConstants) {
if (printComma) {
stream << ", ";
} else {
printComma = true;
}
stream << constant.get().getName() << " (" << constant.get().getType() << ")";
}
stream << ".";
STORM_LOG_THROW(false, storm::exceptions::InvalidArgumentException, "Program still contains these undefined constants: " + stream.str());
} else if (std::is_same<ValueType, storm::RationalFunction>::value && !this->program.hasUndefinedConstantsOnlyInUpdateProbabilitiesAndRewards()) {
STORM_LOG_THROW(false, storm::exceptions::InvalidArgumentException, "The program contains undefined constants that appear in some places other than update probabilities and reward value expressions, which is not admitted.");
}
// If the set of labels we are supposed to built is restricted, we need to remove the other labels from the program.
if (options.labelsToBuild) {
if (!options.buildAllLabels) {
this->program.filterLabels(options.labelsToBuild.get());
}
}
// If we need to build labels for expressions that may appear in some formula, we need to add appropriate
// labels to the program.
if (options.expressionLabels) {
std::map<storm::expressions::Variable, storm::expressions::Expression> constantsSubstitution = this->program.getConstantsSubstitution();
for (auto const& expression : options.expressionLabels.get()) {
std::stringstream stream;
stream << expression.substitute(constantsSubstitution);
std::string name = stream.str();
if (!this->program.hasLabel(name)) {
this->program.addLabel(name, expression);
}
}
}
// Now that the program is fixed, we we need to substitute all constants with their concrete value.
this->program = this->program.substituteConstants();
// Create the variable information for the transformed program.
this->variableInformation = VariableInformation(this->program);
// Create the internal state storage.
this->internalStateInformation = InternalStateInformation(variableInformation.getTotalBitOffset(true));
}
template <typename ValueType, typename RewardModelType, typename StateType>
typename ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::StateInformation const& ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::getStateInformation() const {
STORM_LOG_THROW(static_cast<bool>(stateInformation), storm::exceptions::InvalidOperationException, "The state information was not properly build.");
return stateInformation.get();
}
template <typename ValueType, typename RewardModelType, typename StateType>
storm::prism::Program const& ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::getTranslatedProgram() const {
return program;
}
template <typename ValueType, typename RewardModelType, typename StateType>
std::shared_ptr<storm::models::sparse::Model<ValueType, RewardModelType>> ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::translate() {
STORM_LOG_DEBUG("Building representation of program:" << std::endl << program << std::endl);
STORM_LOG_DEBUG("Exploration order is: " << options.explorationOrder);
// Select the appropriate reward models (after the constants have been substituted).
std::vector<std::reference_wrapper<storm::prism::RewardModel const>> selectedRewardModels;
// First, we make sure that all selected reward models actually exist.
for (auto const& rewardModelName : options.rewardModelsToBuild) {
STORM_LOG_THROW(rewardModelName.empty() || program.hasRewardModel(rewardModelName), storm::exceptions::InvalidArgumentException, "Model does not possess a reward model with the name '" << rewardModelName << "'.");
}
for (auto const& rewardModel : program.getRewardModels()) {
if (options.buildAllRewardModels || options.rewardModelsToBuild.find(rewardModel.getName()) != options.rewardModelsToBuild.end()) {
selectedRewardModels.push_back(rewardModel);
}
}
// If no reward model was selected until now and a referenced reward model appears to be unique, we build
// the only existing reward model (given that no explicit name was given for the referenced reward model).
if (selectedRewardModels.empty() && program.getNumberOfRewardModels() == 1 && options.rewardModelsToBuild.size() == 1 && *options.rewardModelsToBuild.begin() == "") {
selectedRewardModels.push_back(program.getRewardModel(0));
}
ModelComponents modelComponents = buildModelComponents(selectedRewardModels);
std::shared_ptr<storm::models::sparse::Model<ValueType, RewardModelType>> result;
switch (program.getModelType()) {
case storm::prism::Program::ModelType::DTMC:
result = std::shared_ptr<storm::models::sparse::Model<ValueType, RewardModelType>>(new storm::models::sparse::Dtmc<ValueType, RewardModelType>(std::move(modelComponents.transitionMatrix), std::move(modelComponents.stateLabeling), std::move(modelComponents.rewardModels), std::move(modelComponents.choiceLabeling)));
break;
case storm::prism::Program::ModelType::CTMC:
result = std::shared_ptr<storm::models::sparse::Model<ValueType, RewardModelType>>(new storm::models::sparse::Ctmc<ValueType, RewardModelType>(std::move(modelComponents.transitionMatrix), std::move(modelComponents.stateLabeling), std::move(modelComponents.rewardModels), std::move(modelComponents.choiceLabeling)));
break;
case storm::prism::Program::ModelType::MDP:
result = std::shared_ptr<storm::models::sparse::Model<ValueType, RewardModelType>>(new storm::models::sparse::Mdp<ValueType, RewardModelType>(std::move(modelComponents.transitionMatrix), std::move(modelComponents.stateLabeling), std::move(modelComponents.rewardModels), std::move(modelComponents.choiceLabeling)));
break;
default:
STORM_LOG_THROW(false, storm::exceptions::WrongFormatException, "Error while creating model from probabilistic program: cannot handle this model type.");
break;
}
return result;
}
template <typename ValueType, typename RewardModelType, typename StateType>
storm::expressions::SimpleValuation ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::unpackStateIntoValuation(storm::storage::BitVector const& currentState) {
storm::expressions::SimpleValuation result(program.getManager().getSharedPointer());
for (auto const& booleanVariable : variableInformation.booleanVariables) {
result.setBooleanValue(booleanVariable.variable, currentState.get(booleanVariable.bitOffset));
}
for (auto const& integerVariable : variableInformation.integerVariables) {
result.setIntegerValue(integerVariable.variable, currentState.getAsInt(integerVariable.bitOffset, integerVariable.bitWidth) + integerVariable.lowerBound);
}
return result;
}
template <typename ValueType, typename RewardModelType, typename StateType>
StateType ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::getOrAddStateIndex(CompressedState const& state) {
uint32_t newIndex = internalStateInformation.numberOfStates;
// Check, if the state was already registered.
std::pair<uint32_t, std::size_t> actualIndexBucketPair = internalStateInformation.stateStorage.findOrAddAndGetBucket(state, newIndex);
if (actualIndexBucketPair.first == newIndex) {
if (options.explorationOrder == ExplorationOrder::Dfs) {
statesToExplore.push_front(state);
// Reserve one slot for the new state in the remapping.
stateRemapping.get().push_back(storm::utility::zero<StateType>());
} else if (options.explorationOrder == ExplorationOrder::Bfs) {
statesToExplore.push_back(state);
} else {
STORM_LOG_ASSERT(false, "Invalid exploration order.");
}
++internalStateInformation.numberOfStates;
}
return actualIndexBucketPair.first;
}
template <typename ValueType, typename RewardModelType, typename StateType>
boost::optional<std::vector<boost::container::flat_set<uint_fast64_t>>> ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::buildMatrices(std::vector<std::reference_wrapper<storm::prism::RewardModel const>> const& selectedRewardModels, storm::storage::SparseMatrixBuilder<ValueType>& transitionMatrixBuilder, std::vector<RewardModelBuilder<typename RewardModelType::ValueType>>& rewardModelBuilders, boost::optional<storm::expressions::Expression> const& terminalExpression) {
// Create choice labels, if requested,
boost::optional<std::vector<boost::container::flat_set<uint_fast64_t>>> choiceLabels;
if (options.buildCommandLabels) {
choiceLabels = std::vector<boost::container::flat_set<uint_fast64_t>>();
}
// Create a generator that is able to expand states.
storm::generator::PrismNextStateGenerator<ValueType, StateType> generator(program, variableInformation, options.buildCommandLabels);
if (terminalExpression) {
generator.setTerminalExpression(terminalExpression.get());
}
for (auto const& rewardModel : selectedRewardModels) {
generator.addRewardModel(rewardModel.get());
}
// Create a callback for the next-state generator to enable it to request the index of states.
std::function<StateType (CompressedState const&)> stateToIdCallback = std::bind(&ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::getOrAddStateIndex, this, std::placeholders::_1);
// If the exploration order is something different from breadth-first, we need to keep track of the remapping
// from state ids to row groups. For this, we actually store the reversed mapping of row groups to state-ids
// and later reverse it.
if (options.explorationOrder != ExplorationOrder::Bfs) {
stateRemapping = std::vector<uint_fast64_t>();
}
// Let the generator create all initial states.
this->internalStateInformation.initialStateIndices = generator.getInitialStates(stateToIdCallback);
// Now explore the current state until there is no more reachable state.
uint_fast64_t currentRowGroup = 0;
uint_fast64_t currentRow = 0;
// Perform a search through the model.
while (!statesToExplore.empty()) {
// Get the first state in the queue.
CompressedState currentState = statesToExplore.front();
StateType currentIndex = internalStateInformation.stateStorage.getValue(currentState);
statesToExplore.pop_front();
// If the exploration order differs from breadth-first, we remember that this row group was actually
// filled with the transitions of a different state.
if (options.explorationOrder != ExplorationOrder::Bfs) {
stateRemapping.get()[currentIndex] = currentRowGroup;
}
STORM_LOG_TRACE("Exploring state with id " << currentIndex << ".");
storm::generator::StateBehavior<ValueType, StateType> behavior = generator.expand(currentState, stateToIdCallback);
// If there is no behavior, we might have to introduce a self-loop.
if (behavior.empty()) {
if (!storm::settings::getModule<storm::settings::modules::MarkovChainSettings>().isDontFixDeadlocksSet() || !behavior.wasExpanded()) {
if (options.buildCommandLabels) {
// Insert empty choice labeling for added self-loop transitions.
choiceLabels.get().push_back(boost::container::flat_set<uint_fast64_t>());
}
if (!generator.isDeterministicModel()) {
transitionMatrixBuilder.newRowGroup(currentRow);
}
transitionMatrixBuilder.addNextValue(currentRow, currentIndex, storm::utility::one<ValueType>());
auto builderIt = rewardModelBuilders.begin();
for (auto const& rewardModel : selectedRewardModels) {
if (rewardModel.get().hasStateRewards()) {
builderIt->stateRewardVector.push_back(storm::utility::zero<ValueType>());
}
if (rewardModel.get().hasStateActionRewards()) {
builderIt->stateActionRewardVector.push_back(storm::utility::zero<ValueType>());
}
++builderIt;
}
++currentRow;
++currentRowGroup;
} else {
std::cout << unpackStateIntoValuation(currentState).toString(true) << std::endl;
STORM_LOG_THROW(false, storm::exceptions::WrongFormatException,
"Error while creating sparse matrix from probabilistic program: found deadlock state. For fixing these, please provide the appropriate option.");
}
} else {
// Add the state rewards to the corresponding reward models.
auto builderIt = rewardModelBuilders.begin();
auto stateRewardIt = behavior.getStateRewards().begin();
for (auto const& rewardModel : selectedRewardModels) {
if (rewardModel.get().hasStateRewards()) {
builderIt->stateRewardVector.push_back(*stateRewardIt);
}
++stateRewardIt;
++builderIt;
}
// If the model is nondeterministic, we need to open a row group.
if (!generator.isDeterministicModel()) {
transitionMatrixBuilder.newRowGroup(currentRow);
}
// Now add all choices.
for (auto const& choice : behavior) {
// Add command labels if requested.
if (options.buildCommandLabels) {
choiceLabels.get().push_back(choice.getChoiceLabels());
}
// Add the probabilistic behavior to the matrix.
for (auto const& stateProbabilityPair : choice) {
transitionMatrixBuilder.addNextValue(currentRow, stateProbabilityPair.first, stateProbabilityPair.second);
}
// Add the rewards to the reward models.
auto builderIt = rewardModelBuilders.begin();
auto choiceRewardIt = choice.getChoiceRewards().begin();
for (auto const& rewardModel : selectedRewardModels) {
if (rewardModel.get().hasStateActionRewards()) {
builderIt->stateActionRewardVector.push_back(*choiceRewardIt);
}
++choiceRewardIt;
++builderIt;
}
++currentRow;
}
++currentRowGroup;
}
}
// If the exploration order was not breadth-first, we need to fix the entries in the matrix according to
// (reversed) mapping of row groups to indices.
if (options.explorationOrder != ExplorationOrder::Bfs) {
STORM_LOG_ASSERT(stateRemapping, "Unable to fix columns without mapping.");
std::vector<uint_fast64_t> const& remapping = stateRemapping.get();
// We need to fix the following entities:
// (a) the transition matrix
// (b) the initial states
// (c) the hash map storing the mapping states -> ids
// Fix (a).
transitionMatrixBuilder.replaceColumns(remapping, 0);
// Fix (b).
std::vector<StateType> newInitialStateIndices(this->internalStateInformation.initialStateIndices.size());
std::transform(this->internalStateInformation.initialStateIndices.begin(), this->internalStateInformation.initialStateIndices.end(), newInitialStateIndices.begin(), [&remapping] (StateType const& state) { return remapping[state]; } );
std::sort(newInitialStateIndices.begin(), newInitialStateIndices.end());
this->internalStateInformation.initialStateIndices = std::move(newInitialStateIndices);
// Fix (c).
this->internalStateInformation.stateStorage.remap([&remapping] (StateType const& state) { return remapping[state]; } );
}
return choiceLabels;
}
template <typename ValueType, typename RewardModelType, typename StateType>
typename ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::ModelComponents ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::buildModelComponents(std::vector<std::reference_wrapper<storm::prism::RewardModel const>> const& selectedRewardModels) {
ModelComponents modelComponents;
// Determine whether we have to combine different choices to one or whether this model can have more than
// one choice per state.
bool deterministicModel = program.isDeterministicModel();
// Prepare the transition matrix builder and the reward model builders.
storm::storage::SparseMatrixBuilder<ValueType> transitionMatrixBuilder(0, 0, 0, false, !deterministicModel, 0);
std::vector<RewardModelBuilder<typename RewardModelType::ValueType>> rewardModelBuilders;
for (auto const& rewardModel : selectedRewardModels) {
rewardModelBuilders.emplace_back(deterministicModel, rewardModel.get().hasStateRewards(), rewardModel.get().hasStateActionRewards(), rewardModel.get().hasTransitionRewards());
}
// If we were asked to treat some states as terminal states, we determine an expression characterizing the
// terminal states of the model that we pass on to the matrix building routine.
boost::optional<storm::expressions::Expression> terminalExpression;
if (options.terminalStates) {
if (options.terminalStates.get().type() == typeid(storm::expressions::Expression)) {
terminalExpression = boost::get<storm::expressions::Expression>(options.terminalStates.get());
} else {
std::string const& labelName = boost::get<std::string>(options.terminalStates.get());
terminalExpression = program.getLabelExpression(labelName);
}
}
if (options.negatedTerminalStates) {
if (options.negatedTerminalStates.get().type() == typeid(storm::expressions::Expression)) {
if (terminalExpression) {
terminalExpression = terminalExpression.get() || !boost::get<storm::expressions::Expression>(options.negatedTerminalStates.get());
} else {
terminalExpression = !boost::get<storm::expressions::Expression>(options.negatedTerminalStates.get());
}
} else {
std::string const& labelName = boost::get<std::string>(options.negatedTerminalStates.get());
if (terminalExpression) {
terminalExpression = terminalExpression.get() || !program.getLabelExpression(labelName);
} else {
terminalExpression = !program.getLabelExpression(labelName);
}
}
}
if (terminalExpression) {
STORM_LOG_TRACE("Making the states satisfying " << terminalExpression.get() << " terminal.");
}
modelComponents.choiceLabeling = buildMatrices(selectedRewardModels, transitionMatrixBuilder, rewardModelBuilders, terminalExpression);
modelComponents.transitionMatrix = transitionMatrixBuilder.build();
// Now finalize all reward models.
auto builderIt = rewardModelBuilders.begin();
for (auto rewardModelIt = selectedRewardModels.begin(), rewardModelIte = selectedRewardModels.end(); rewardModelIt != rewardModelIte; ++rewardModelIt, ++builderIt) {
modelComponents.rewardModels.emplace(rewardModelIt->get().getName(), builderIt->build(modelComponents.transitionMatrix.getRowCount(), modelComponents.transitionMatrix.getColumnCount(), modelComponents.transitionMatrix.getRowGroupCount()));
}
// Build the state labeling.
modelComponents.stateLabeling = buildStateLabeling();
// Finally -- if requested -- build the state information that can be retrieved from the outside.
if (options.buildStateInformation) {
stateInformation = StateInformation(internalStateInformation.numberOfStates);
for (auto const& bitVectorIndexPair : internalStateInformation.stateStorage) {
stateInformation.get().valuations[bitVectorIndexPair.second] = unpackStateIntoValuation(bitVectorIndexPair.first);
}
}
return modelComponents;
}
template <typename ValueType, typename RewardModelType, typename StateType>
storm::models::sparse::StateLabeling ExplicitPrismModelBuilder<ValueType, RewardModelType, StateType>::buildStateLabeling() {
storm::generator::PrismStateLabelingGenerator<ValueType, StateType> generator(program, variableInformation);
return generator.generate(internalStateInformation.stateStorage, internalStateInformation.initialStateIndices);
}
// Explicitly instantiate the class.
template class ExplicitPrismModelBuilder<double, storm::models::sparse::StandardRewardModel<double>, uint32_t>;
#ifdef STORM_HAVE_CARL
template class ExplicitPrismModelBuilder<double, storm::models::sparse::StandardRewardModel<storm::Interval>, uint32_t>;
template class ExplicitPrismModelBuilder<RationalFunction, storm::models::sparse::StandardRewardModel<RationalFunction>, uint32_t>;
#endif
}
}