#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/GeneralSettings.h"
#include "src/utility/prism.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(), transitionRewardMatrixBuilder(0, 0, 0, false, !deterministicModel, 0) {
// 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);
}
boost::optional<storm::storage::SparseMatrix<ValueType>> optionalTransitionRewardMatrix;
if (hasTransitionRewards) {
optionalTransitionRewardMatrix = transitionRewardMatrixBuilder.build(rowCount, columnCount, rowGroupCount);
}
return storm::models::sparse::StandardRewardModel<ValueType>(std::move(optionalStateRewardVector), std::move(optionalStateActionRewardVector), std::move(optionalTransitionRewardMatrix));
}
bool hasStateRewards;
bool hasStateActionRewards;
bool hasTransitionRewards;
// The state reward vector.
std::vector<ValueType> stateRewardVector;
// The state-action reward vector.
std::vector<ValueType> stateActionRewardVector;
// A builder that is used for constructing the transition reward matrix.
storm::storage::SparseMatrixBuilder<ValueType> transitionRewardMatrixBuilder;
};
template <typename ValueType, typename RewardModelType, typename IndexType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::StateInformation::StateInformation(uint_fast64_t numberOfStates) : valuations(numberOfStates) {
// Intentionally left empty.
}
template <typename ValueType, typename RewardModelType, typename IndexType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::InternalStateInformation::InternalStateInformation(uint64_t bitsPerState) : stateStorage(bitsPerState, 10000000), bitsPerState(bitsPerState), reachableStates() {
// Intentionally left empty.
}
template <typename ValueType, typename RewardModelType, typename IndexType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::VariableInformation::BooleanVariableInformation::BooleanVariableInformation(storm::expressions::Variable const& variable, bool initialValue, uint_fast64_t bitOffset) : variable(variable), initialValue(initialValue), bitOffset(bitOffset) {
// Intentionally left empty.
}
template <typename ValueType, typename RewardModelType, typename IndexType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::VariableInformation::IntegerVariableInformation::IntegerVariableInformation(storm::expressions::Variable const& variable, int_fast64_t initialValue, int_fast64_t lowerBound, int_fast64_t upperBound, uint_fast64_t bitOffset, uint_fast64_t bitWidth) : variable(variable), initialValue(initialValue), lowerBound(lowerBound), upperBound(upperBound), bitOffset(bitOffset), bitWidth(bitWidth) {
// Intentionally left empty.
}
template <typename ValueType, typename RewardModelType, typename IndexType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::VariableInformation::VariableInformation(storm::expressions::ExpressionManager const& manager) : manager(manager) {
// Intentionally left empty.
}
template <typename ValueType, typename RewardModelType, typename IndexType>
uint_fast64_t ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::VariableInformation::getBitOffset(storm::expressions::Variable const& variable) const {
auto const& booleanIndex = booleanVariableToIndexMap.find(variable);
if (booleanIndex != booleanVariableToIndexMap.end()) {
return booleanVariables[booleanIndex->second].bitOffset;
}
auto const& integerIndex = integerVariableToIndexMap.find(variable);
if (integerIndex != integerVariableToIndexMap.end()) {
return integerVariables[integerIndex->second].bitOffset;
}
STORM_LOG_THROW(false, storm::exceptions::InvalidArgumentException, "Cannot look-up bit index of unknown variable.");
}
template <typename ValueType, typename RewardModelType, typename IndexType>
uint_fast64_t ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::VariableInformation::getBitWidth(storm::expressions::Variable const& variable) const {
auto const& integerIndex = integerVariableToIndexMap.find(variable);
if (integerIndex != integerVariableToIndexMap.end()) {
return integerVariables[integerIndex->second].bitWidth;
}
STORM_LOG_THROW(false, storm::exceptions::InvalidArgumentException, "Cannot look-up bit width of unknown variable.");
}
template <typename ValueType, typename RewardModelType, typename IndexType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::ModelComponents::ModelComponents() : transitionMatrix(), stateLabeling(), rewardModels(), choiceLabeling() {
// Intentionally left empty.
}
template <typename ValueType, typename RewardModelType, typename IndexType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::Options::Options() : buildCommandLabels(false), buildAllRewardModels(true), buildStateInformation(false), rewardModelsToBuild(), constantDefinitions(), buildAllLabels(true), labelsToBuild(), expressionLabels(), terminalStates(), negatedTerminalStates() {
// Intentionally left empty.
}
template <typename ValueType, typename RewardModelType, typename IndexType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::Options::Options(storm::logic::Formula const& formula) : 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 IndexType>
ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::Options::Options(std::vector<std::shared_ptr<const storm::logic::Formula>> const& formulas) : 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 IndexType>
void ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::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 IndexType>
void ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::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 IndexType>
void ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::Options::addConstantDefinitionsFromString(storm::prism::Program const& program, std::string const& constantDefinitionString) {
constantDefinitions = storm::utility::prism::parseConstantDefinitionString(program, constantDefinitionString);
}
template <typename ValueType, typename RewardModelType, typename IndexType>
typename ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::StateInformation const& ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::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 IndexType>
storm::prism::Program const& ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::getTranslatedProgram() const {
return preparedProgram.get();
}
template <typename ValueType, typename RewardModelType, typename IndexType>
std::shared_ptr<storm::models::sparse::Model<ValueType, RewardModelType>> ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::translateProgram(storm::prism::Program program, Options const& options) {
// Start by defining the undefined constants in the model.
if (options.constantDefinitions) {
preparedProgram = program.defineUndefinedConstants(options.constantDefinitions.get());
} else {
preparedProgram = program;
}
// If the program still contains undefined constants and we are not in a parametric setting, assemble an appropriate error message.
#ifdef STORM_HAVE_CARL
// If the program either has undefined constants or we are building a parametric model, but the parameters
// not only appear in the probabilities, we re
if (!std::is_same<ValueType, storm::RationalFunction>::value && preparedProgram->hasUndefinedConstants()) {
#else
if (preparedProgram->hasUndefinedConstants()) {
#endif
std::vector<std::reference_wrapper<storm::prism::Constant const>> undefinedConstants = preparedProgram->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());
#ifdef STORM_HAVE_CARL
} else if (std::is_same<ValueType, storm::RationalFunction>::value && !preparedProgram->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.");
#endif
}
// 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) {
preparedProgram->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 = preparedProgram->getConstantsSubstitution();
for (auto const& expression : options.expressionLabels.get()) {
std::stringstream stream;
stream << expression.substitute(constantsSubstitution);
std::string name = stream.str();
if (!preparedProgram->hasLabel(name)) {
preparedProgram->addLabel(name, expression);
}
}
}
// Now that the program is fixed, we we need to substitute all constants with their concrete value.
preparedProgram = preparedProgram->substituteConstants();
STORM_LOG_DEBUG("Building representation of program:" << std::endl << *preparedProgram << std::endl);
// 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() || preparedProgram->hasRewardModel(rewardModelName), storm::exceptions::InvalidArgumentException, "Model does not possess a reward model with the name '" << rewardModelName << "'.");
}
for (auto const& rewardModel : preparedProgram->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() && preparedProgram->getNumberOfRewardModels() == 1 && options.rewardModelsToBuild.size() == 1 && *options.rewardModelsToBuild.begin() == "") {
selectedRewardModels.push_back(preparedProgram->getRewardModel(0));
}
ModelComponents modelComponents = buildModelComponents(*preparedProgram, selectedRewardModels, options);
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 IndexType>
void ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::unpackStateIntoEvaluator(storm::storage::BitVector const& currentState, VariableInformation const& variableInformation, storm::expressions::ExpressionEvaluator<ValueType>& evaluator) {
for (auto const& booleanVariable : variableInformation.booleanVariables) {
evaluator.setBooleanValue(booleanVariable.variable, currentState.get(booleanVariable.bitOffset));
}
for (auto const& integerVariable : variableInformation.integerVariables) {
evaluator.setIntegerValue(integerVariable.variable, currentState.getAsInt(integerVariable.bitOffset, integerVariable.bitWidth) + integerVariable.lowerBound);
}
}
template <typename ValueType, typename RewardModelType, typename IndexType>
storm::expressions::SimpleValuation ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::unpackStateIntoValuation(storm::storage::BitVector const& currentState, VariableInformation const& variableInformation) {
storm::expressions::SimpleValuation result(variableInformation.manager.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 IndexType>
typename ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::CompressedState ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::applyUpdate(VariableInformation const& variableInformation, CompressedState const& state, storm::prism::Update const& update, storm::expressions::ExpressionEvaluator<ValueType> const& evaluator) {
return applyUpdate(variableInformation, state, state, update, evaluator);
}
template <typename ValueType, typename RewardModelType, typename IndexType>
typename ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::CompressedState ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::applyUpdate(VariableInformation const& variableInformation, CompressedState const& state, CompressedState const& baseState, storm::prism::Update const& update, storm::expressions::ExpressionEvaluator<ValueType> const& evaluator) {
CompressedState newState(state);
auto assignmentIt = update.getAssignments().begin();
auto assignmentIte = update.getAssignments().end();
// Iterate over all boolean assignments and carry them out.
auto boolIt = variableInformation.booleanVariables.begin();
for (; assignmentIt != assignmentIte && assignmentIt->getExpression().hasBooleanType(); ++assignmentIt) {
while (assignmentIt->getVariable() != boolIt->variable) {
++boolIt;
}
newState.set(boolIt->bitOffset, evaluator.asBool(assignmentIt->getExpression()));
}
// Iterate over all integer assignments and carry them out.
auto integerIt = variableInformation.integerVariables.begin();
for (; assignmentIt != assignmentIte && assignmentIt->getExpression().hasIntegerType(); ++assignmentIt) {
while (assignmentIt->getVariable() != integerIt->variable) {
++integerIt;
}
int_fast64_t assignedValue = evaluator.asInt(assignmentIt->getExpression());
STORM_LOG_THROW(assignedValue <= integerIt->upperBound, storm::exceptions::WrongFormatException, "The update " << update << " leads to an out-of-bounds value (" << assignedValue << ") for the variable '" << assignmentIt->getVariableName() << "'.");
newState.setFromInt(integerIt->bitOffset, integerIt->bitWidth, assignedValue - integerIt->lowerBound);
STORM_LOG_ASSERT(static_cast<int_fast64_t>(newState.getAsInt(integerIt->bitOffset, integerIt->bitWidth)) + integerIt->lowerBound == assignedValue, "Writing to the bit vector bucket failed (read " << newState.getAsInt(integerIt->bitOffset, integerIt->bitWidth) << " but wrote " << assignedValue << ").");
}
// Check that we processed all assignments.
STORM_LOG_ASSERT(assignmentIt == assignmentIte, "Not all assignments were consumed.");
return newState;
}
template <typename ValueType, typename RewardModelType, typename IndexType>
IndexType ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::getOrAddStateIndex(CompressedState const& state, InternalStateInformation& internalStateInformation, std::queue<storm::storage::BitVector>& stateQueue) {
uint32_t newIndex = internalStateInformation.reachableStates.size();
// Check, if the state was already registered.
std::pair<uint32_t, std::size_t> actualIndexBucketPair = internalStateInformation.stateStorage.findOrAddAndGetBucket(state, newIndex);
if (actualIndexBucketPair.first == newIndex) {
stateQueue.push(state);
internalStateInformation.reachableStates.push_back(state);
}
return actualIndexBucketPair.first;
}
template <typename ValueType, typename RewardModelType, typename IndexType>
boost::optional<std::vector<std::vector<std::reference_wrapper<storm::prism::Command const>>>> ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::getActiveCommandsByActionIndex(storm::prism::Program const& program,storm::expressions::ExpressionEvaluator<ValueType> const& evaluator, uint_fast64_t const& actionIndex) {
boost::optional<std::vector<std::vector<std::reference_wrapper<storm::prism::Command const>>>> result((std::vector<std::vector<std::reference_wrapper<storm::prism::Command const>>>()));
// Iterate over all modules.
for (uint_fast64_t i = 0; i < program.getNumberOfModules(); ++i) {
storm::prism::Module const& module = program.getModule(i);
// If the module has no command labeled with the given action, we can skip this module.
if (!module.hasActionIndex(actionIndex)) {
continue;
}
std::set<uint_fast64_t> const& commandIndices = module.getCommandIndicesByActionIndex(actionIndex);
// If the module contains the action, but there is no command in the module that is labeled with
// this action, we don't have any feasible command combinations.
if (commandIndices.empty()) {
return boost::optional<std::vector<std::vector<std::reference_wrapper<storm::prism::Command const>>>>();
}
std::vector<std::reference_wrapper<storm::prism::Command const>> commands;
// Look up commands by their indices and add them if the guard evaluates to true in the given state.
for (uint_fast64_t commandIndex : commandIndices) {
storm::prism::Command const& command = module.getCommand(commandIndex);
if (evaluator.asBool(command.getGuardExpression())) {
commands.push_back(command);
}
}
// If there was no enabled command although the module has some command with the required action label,
// we must not return anything.
if (commands.size() == 0) {
return boost::optional<std::vector<std::vector<std::reference_wrapper<storm::prism::Command const>>>>();
}
result.get().push_back(std::move(commands));
}
return result;
}
template <typename ValueType, typename RewardModelType, typename IndexType>
std::vector<Choice<ValueType>> ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::getUnlabeledTransitions(storm::prism::Program const& program, bool discreteTimeModel, InternalStateInformation& internalStateInformation, VariableInformation const& variableInformation, storm::storage::BitVector const& currentState, bool choiceLabeling, storm::expressions::ExpressionEvaluator<ValueType> const& evaluator, std::queue<storm::storage::BitVector>& stateQueue, storm::utility::ConstantsComparator<ValueType> const& comparator) {
std::vector<Choice<ValueType>> result;
// Iterate over all modules.
for (uint_fast64_t i = 0; i < program.getNumberOfModules(); ++i) {
storm::prism::Module const& module = program.getModule(i);
// Iterate over all commands.
for (uint_fast64_t j = 0; j < module.getNumberOfCommands(); ++j) {
storm::prism::Command const& command = module.getCommand(j);
// Only consider unlabeled commands.
if (command.isLabeled()) continue;
// Skip the command, if it is not enabled.
if (!evaluator.asBool(command.getGuardExpression())) {
continue;
}
result.push_back(Choice<ValueType>(0, choiceLabeling));
Choice<ValueType>& choice = result.back();
// Remember the command labels only if we were asked to.
if (choiceLabeling) {
choice.addChoiceLabel(command.getGlobalIndex());
}
// Iterate over all updates of the current command.
ValueType probabilitySum = storm::utility::zero<ValueType>();
for (uint_fast64_t k = 0; k < command.getNumberOfUpdates(); ++k) {
storm::prism::Update const& update = command.getUpdate(k);
// Obtain target state index and add it to the list of known states. If it has not yet been
// seen, we also add it to the set of states that have yet to be explored.
uint32_t stateIndex = getOrAddStateIndex(applyUpdate(variableInformation, currentState, update, evaluator), internalStateInformation, stateQueue);
// Update the choice by adding the probability/target state to it.
ValueType probability = evaluator.asRational(update.getLikelihoodExpression());
choice.addProbability(stateIndex, probability);
probabilitySum += probability;
}
// Check that the resulting distribution is in fact a distribution.
STORM_LOG_THROW(!discreteTimeModel || comparator.isOne(probabilitySum), storm::exceptions::WrongFormatException, "Probabilities do not sum to one for command '" << command << "' (actually sum to " << probabilitySum << ").");
}
}
return result;
}
template <typename ValueType, typename RewardModelType, typename IndexType>
std::vector<Choice<ValueType>> ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::getLabeledTransitions(storm::prism::Program const& program, bool discreteTimeModel, InternalStateInformation& internalStateInformation, VariableInformation const& variableInformation, storm::storage::BitVector const& currentState, bool choiceLabeling, storm::expressions::ExpressionEvaluator<ValueType> const& evaluator, std::queue<storm::storage::BitVector>& stateQueue, storm::utility::ConstantsComparator<ValueType> const& comparator) {
std::vector<Choice<ValueType>> result;
for (uint_fast64_t actionIndex : program.getSynchronizingActionIndices()) {
boost::optional<std::vector<std::vector<std::reference_wrapper<storm::prism::Command const>>>> optionalActiveCommandLists = getActiveCommandsByActionIndex(program, evaluator, actionIndex);
// Only process this action label, if there is at least one feasible solution.
if (optionalActiveCommandLists) {
std::vector<std::vector<std::reference_wrapper<storm::prism::Command const>>> const& activeCommandList = optionalActiveCommandLists.get();
std::vector<std::vector<std::reference_wrapper<storm::prism::Command const>>::const_iterator> iteratorList(activeCommandList.size());
// Initialize the list of iterators.
for (size_t i = 0; i < activeCommandList.size(); ++i) {
iteratorList[i] = activeCommandList[i].cbegin();
}
// As long as there is one feasible combination of commands, keep on expanding it.
bool done = false;
while (!done) {
std::unordered_map<CompressedState, ValueType>* currentTargetStates = new std::unordered_map<CompressedState, ValueType>();
std::unordered_map<CompressedState, ValueType>* newTargetStates = new std::unordered_map<CompressedState, ValueType>();
currentTargetStates->emplace(currentState, storm::utility::one<ValueType>());
for (uint_fast64_t i = 0; i < iteratorList.size(); ++i) {
storm::prism::Command const& command = *iteratorList[i];
for (uint_fast64_t j = 0; j < command.getNumberOfUpdates(); ++j) {
storm::prism::Update const& update = command.getUpdate(j);
for (auto const& stateProbabilityPair : *currentTargetStates) {
// Compute the new state under the current update and add it to the set of new target states.
CompressedState newTargetState = applyUpdate(variableInformation, stateProbabilityPair.first, currentState, update, evaluator);
newTargetStates->emplace(newTargetState, stateProbabilityPair.second * evaluator.asRational(update.getLikelihoodExpression()));
}
}
// If there is one more command to come, shift the target states one time step back.
if (i < iteratorList.size() - 1) {
delete currentTargetStates;
currentTargetStates = newTargetStates;
newTargetStates = new std::unordered_map<CompressedState, ValueType>();
}
}
// At this point, we applied all commands of the current command combination and newTargetStates
// contains all target states and their respective probabilities. That means we are now ready to
// add the choice to the list of transitions.
result.push_back(Choice<ValueType>(actionIndex, choiceLabeling));
// Now create the actual distribution.
Choice<ValueType>& choice = result.back();
// Remember the command labels only if we were asked to.
if (choiceLabeling) {
// Add the labels of all commands to this choice.
for (uint_fast64_t i = 0; i < iteratorList.size(); ++i) {
choice.addChoiceLabel(iteratorList[i]->get().getGlobalIndex());
}
}
ValueType probabilitySum = storm::utility::zero<ValueType>();
for (auto const& stateProbabilityPair : *newTargetStates) {
uint32_t actualIndex = getOrAddStateIndex(stateProbabilityPair.first, internalStateInformation, stateQueue);
choice.addProbability(actualIndex, stateProbabilityPair.second);
probabilitySum += stateProbabilityPair.second;
}
// Check that the resulting distribution is in fact a distribution.
STORM_LOG_THROW(!discreteTimeModel || !comparator.isConstant(probabilitySum) || comparator.isOne(probabilitySum), storm::exceptions::WrongFormatException, "Sum of update probabilities do not some to one for some command (actually sum to " << probabilitySum << ").");
// Dispose of the temporary maps.
delete currentTargetStates;
delete newTargetStates;
// Now, check whether there is one more command combination to consider.
bool movedIterator = false;
for (int_fast64_t j = iteratorList.size() - 1; j >= 0; --j) {
++iteratorList[j];
if (iteratorList[j] != activeCommandList[j].end()) {
movedIterator = true;
} else {
// Reset the iterator to the beginning of the list.
iteratorList[j] = activeCommandList[j].begin();
}
}
done = !movedIterator;
}
}
}
return result;
}
template <typename ValueType, typename RewardModelType, typename IndexType>
boost::optional<std::vector<boost::container::flat_set<uint_fast64_t>>> ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::buildMatrices(storm::prism::Program const& program, VariableInformation const& variableInformation, std::vector<std::reference_wrapper<storm::prism::RewardModel const>> const& selectedRewardModels, InternalStateInformation& internalStateInformation, bool commandLabels, bool deterministicModel, bool discreteTimeModel, 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 (commandLabels) {
choiceLabels = std::vector<boost::container::flat_set<uint_fast64_t>>();
}
// A comparator that can be used to check whether we actually have distributions.
storm::utility::ConstantsComparator<ValueType> comparator;
// Initialize a queue and insert the initial state.
std::queue<storm::storage::BitVector> stateQueue;
CompressedState initialState(internalStateInformation.bitsPerState);
// We need to initialize the values of the variables to their initial value.
for (auto const& booleanVariable : variableInformation.booleanVariables) {
initialState.set(booleanVariable.bitOffset, booleanVariable.initialValue);
}
for (auto const& integerVariable : variableInformation.integerVariables) {
initialState.setFromInt(integerVariable.bitOffset, integerVariable.bitWidth, static_cast<uint_fast64_t>(integerVariable.initialValue - integerVariable.lowerBound));
}
// At this point, we determine whether there are reward models with state-action rewards, because we might
// want to know that quickly later on.
bool hasStateActionRewards = false;
for (auto rewardModelIt = selectedRewardModels.begin(), rewardModelIte = selectedRewardModels.end(); rewardModelIt != rewardModelIte; ++rewardModelIt) {
if (rewardModelIt->get().hasStateActionRewards()) {
hasStateActionRewards = true;
break;
}
}
// Insert the initial state in the global state to index mapping and state queue.
uint32_t stateIndex = getOrAddStateIndex(initialState, internalStateInformation, stateQueue);
internalStateInformation.initialStateIndices.push_back(stateIndex);
// Now explore the current state until there is no more reachable state.
uint_fast64_t currentRow = 0;
// The evaluator used to determine the truth value of guards and predicates in the *current* state.
storm::expressions::ExpressionEvaluator<ValueType> evaluator(program.getManager());
// Perform a BFS through the model.
while (!stateQueue.empty()) {
// Get the current state and unpack it.
storm::storage::BitVector currentState = stateQueue.front();
stateQueue.pop();
IndexType stateIndex = internalStateInformation.stateStorage.getValue(currentState);
STORM_LOG_TRACE("Exploring state with id " << stateIndex << ".");
unpackStateIntoEvaluator(currentState, variableInformation, evaluator);
// If a terminal expression was given, we check whether the current state needs to be explored further.
std::vector<Choice<ValueType>> allUnlabeledChoices;
std::vector<Choice<ValueType>> allLabeledChoices;
bool deadlockOnPurpose = false;
if (terminalExpression && evaluator.asBool(terminalExpression.get())) {
//std::cout << unpackStateIntoValuation(currentState, variableInformation).toString(true) << std::endl;
//allUnlabeledChoices = getUnlabeledTransitions(program, discreteTimeModel, internalStateInformation, variableInformation, currentState, commandLabels, evaluator, stateQueue, comparator);
//allLabeledChoices = getLabeledTransitions(program, discreteTimeModel, internalStateInformation, variableInformation, currentState, commandLabels, evaluator, stateQueue, comparator);
// Nothing to do in this case.
deadlockOnPurpose = true;
} else {
// Retrieve all choices for the current state.
allUnlabeledChoices = getUnlabeledTransitions(program, discreteTimeModel, internalStateInformation, variableInformation, currentState, commandLabels, evaluator, stateQueue, comparator);
allLabeledChoices = getLabeledTransitions(program, discreteTimeModel, internalStateInformation, variableInformation, currentState, commandLabels, evaluator, stateQueue, comparator);
}
uint_fast64_t totalNumberOfChoices = allUnlabeledChoices.size() + allLabeledChoices.size();
// If the current state does not have a single choice, we equip it with a self-loop if that was
// requested and issue an error otherwise.
if (totalNumberOfChoices == 0) {
if (!storm::settings::generalSettings().isDontFixDeadlocksSet() || deadlockOnPurpose) {
if (commandLabels) {
// Insert empty choice labeling for added self-loop transitions.
choiceLabels.get().push_back(boost::container::flat_set<uint_fast64_t>());
}
if (!deterministicModel) {
transitionMatrixBuilder.newRowGroup(currentRow);
}
transitionMatrixBuilder.addNextValue(currentRow, stateIndex, storm::utility::one<ValueType>());
auto builderIt = rewardModelBuilders.begin();
for (auto rewardModelIt = selectedRewardModels.begin(), rewardModelIte = selectedRewardModels.end(); rewardModelIt != rewardModelIte; ++rewardModelIt, ++builderIt) {
if (rewardModelIt->get().hasStateRewards()) {
builderIt->stateRewardVector.push_back(storm::utility::zero<ValueType>());
}
if (rewardModelIt->get().hasStateActionRewards()) {
builderIt->stateActionRewardVector.push_back(storm::utility::zero<ValueType>());
}
}
++currentRow;
} else {
std::cout << unpackStateIntoValuation(currentState, variableInformation).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 if (totalNumberOfChoices == 1) {
if (!deterministicModel) {
transitionMatrixBuilder.newRowGroup(currentRow);
}
bool labeledChoice = allUnlabeledChoices.empty() ? true : false;
Choice<ValueType> const& globalChoice = labeledChoice ? allLabeledChoices.front() : allUnlabeledChoices.front();
auto builderIt = rewardModelBuilders.begin();
for (auto rewardModelIt = selectedRewardModels.begin(), rewardModelIte = selectedRewardModels.end(); rewardModelIt != rewardModelIte; ++rewardModelIt, ++builderIt) {
if (rewardModelIt->get().hasStateRewards()) {
builderIt->stateRewardVector.push_back(storm::utility::zero<ValueType>());
for (auto const& stateReward : rewardModelIt->get().getStateRewards()) {
if (evaluator.asBool(stateReward.getStatePredicateExpression())) {
builderIt->stateRewardVector.back() += ValueType(evaluator.asRational(stateReward.getRewardValueExpression()));
}
}
}
if (rewardModelIt->get().hasStateActionRewards()) {
builderIt->stateActionRewardVector.push_back(storm::utility::zero<ValueType>());
for (auto const& stateActionReward : rewardModelIt->get().getStateActionRewards()) {
if ((labeledChoice && stateActionReward.isLabeled() && stateActionReward.getActionIndex() == globalChoice.getActionIndex()) || (!labeledChoice && !stateActionReward.isLabeled())) {
if (evaluator.asBool(stateActionReward.getStatePredicateExpression())) {
builderIt->stateActionRewardVector.back() += ValueType(evaluator.asRational(stateActionReward.getRewardValueExpression()));
}
}
}
}
}
for (auto const& stateProbabilityPair : globalChoice) {
transitionMatrixBuilder.addNextValue(currentRow, stateProbabilityPair.first, stateProbabilityPair.second);
}
if (commandLabels) {
// Now add the resulting distribution as the only choice of the current state.
choiceLabels.get().push_back(globalChoice.getChoiceLabels());
}
++currentRow;
} else {
// Then, based on whether the model is deterministic or not, either add the choices individually
// or compose them to one choice.
if (deterministicModel) {
Choice<ValueType> globalChoice;
// We need to prepare the entries of those vectors that are going to be used.
auto builderIt = rewardModelBuilders.begin();
for (auto rewardModelIt = selectedRewardModels.begin(), rewardModelIte = selectedRewardModels.end(); rewardModelIt != rewardModelIte; ++rewardModelIt, ++builderIt) {
if (rewardModelIt->get().hasStateRewards()) {
builderIt->stateRewardVector.push_back(storm::utility::zero<ValueType>());
for (auto const& stateReward : rewardModelIt->get().getStateRewards()) {
if (evaluator.asBool(stateReward.getStatePredicateExpression())) {
builderIt->stateRewardVector.back() += ValueType(evaluator.asRational(stateReward.getRewardValueExpression()));
}
}
}
if (rewardModelIt->get().hasStateActionRewards()) {
builderIt->stateActionRewardVector.push_back(storm::utility::zero<ValueType>());
}
}
// If there is one state-action reward model, we need to scale the rewards according to the
// multiple choices.
ValueType totalExitMass = storm::utility::zero<ValueType>();
if (hasStateActionRewards) {
if (discreteTimeModel) {
totalExitMass = static_cast<ValueType>(totalNumberOfChoices);
} else {
// In the CTMC, we need to compute the exit rate of the state here, sin
for (auto const& choice : allUnlabeledChoices) {
totalExitMass += choice.getTotalMass();
}
for (auto const& choice : allLabeledChoices) {
totalExitMass += choice.getTotalMass();
}
}
}
// Combine all the choices and scale them with the total number of choices of the current state.
for (auto const& choice : allUnlabeledChoices) {
if (commandLabels) {
globalChoice.addChoiceLabels(choice.getChoiceLabels());
}
auto builderIt = rewardModelBuilders.begin();
for (auto rewardModelIt = selectedRewardModels.begin(), rewardModelIte = selectedRewardModels.end(); rewardModelIt != rewardModelIte; ++rewardModelIt, ++builderIt) {
if (rewardModelIt->get().hasStateActionRewards()) {
for (auto const& stateActionReward : rewardModelIt->get().getStateActionRewards()) {
if (!stateActionReward.isLabeled()) {
if (evaluator.asBool(stateActionReward.getStatePredicateExpression())) {
builderIt->stateActionRewardVector.back() += ValueType(evaluator.asRational(stateActionReward.getRewardValueExpression())) * choice.getTotalMass() / totalExitMass;
}
}
}
}
}
for (auto const& stateProbabilityPair : choice) {
if (discreteTimeModel) {
globalChoice.getOrAddEntry(stateProbabilityPair.first) += stateProbabilityPair.second / totalNumberOfChoices;
} else {
globalChoice.getOrAddEntry(stateProbabilityPair.first) += stateProbabilityPair.second;
}
}
}
for (auto const& choice : allLabeledChoices) {
if (commandLabels) {
globalChoice.addChoiceLabels(choice.getChoiceLabels());
}
auto builderIt = rewardModelBuilders.begin();
for (auto rewardModelIt = selectedRewardModels.begin(), rewardModelIte = selectedRewardModels.end(); rewardModelIt != rewardModelIte; ++rewardModelIt, ++builderIt) {
if (rewardModelIt->get().hasStateActionRewards()) {
for (auto const& stateActionReward : rewardModelIt->get().getStateActionRewards()) {
if (stateActionReward.isLabeled() && stateActionReward.getActionIndex() == choice.getActionIndex()) {
if (evaluator.asBool(stateActionReward.getStatePredicateExpression())) {
builderIt->stateActionRewardVector.back() += ValueType(evaluator.asRational(stateActionReward.getRewardValueExpression())) * choice.getTotalMass() / totalExitMass;
}
}
}
}
}
for (auto const& stateProbabilityPair : choice) {
if (discreteTimeModel) {
globalChoice.getOrAddEntry(stateProbabilityPair.first) += stateProbabilityPair.second / totalNumberOfChoices;
} else {
globalChoice.getOrAddEntry(stateProbabilityPair.first) += stateProbabilityPair.second;
}
}
}
if (commandLabels) {
// Now add the resulting distribution as the only choice of the current state.
choiceLabels.get().push_back(globalChoice.getChoiceLabels());
}
for (auto const& stateProbabilityPair : globalChoice) {
transitionMatrixBuilder.addNextValue(currentRow, stateProbabilityPair.first, stateProbabilityPair.second);
}
++currentRow;
} else {
// If the model is nondeterministic, we add all choices individually.
transitionMatrixBuilder.newRowGroup(currentRow);
auto builderIt = rewardModelBuilders.begin();
for (auto rewardModelIt = selectedRewardModels.begin(), rewardModelIte = selectedRewardModels.end(); rewardModelIt != rewardModelIte; ++rewardModelIt, ++builderIt) {
if (rewardModelIt->get().hasStateRewards()) {
builderIt->stateRewardVector.push_back(storm::utility::zero<ValueType>());
for (auto const& stateReward : rewardModelIt->get().getStateRewards()) {
if (evaluator.asBool(stateReward.getStatePredicateExpression())) {
builderIt->stateRewardVector.back() += ValueType(evaluator.asRational(stateReward.getRewardValueExpression()));
}
}
}
}
// First, process all unlabeled choices.
for (auto const& choice : allUnlabeledChoices) {
std::map<uint_fast64_t, ValueType> stateToRewardMap;
if (commandLabels) {
choiceLabels.get().emplace_back(std::move(choice.getChoiceLabels()));
}
auto builderIt = rewardModelBuilders.begin();
for (auto rewardModelIt = selectedRewardModels.begin(), rewardModelIte = selectedRewardModels.end(); rewardModelIt != rewardModelIte; ++rewardModelIt, ++builderIt) {
if (rewardModelIt->get().hasStateActionRewards()) {
builderIt->stateActionRewardVector.push_back(storm::utility::zero<ValueType>());
for (auto const& stateActionReward : rewardModelIt->get().getStateActionRewards()) {
if (!stateActionReward.isLabeled()) {
if (evaluator.asBool(stateActionReward.getStatePredicateExpression())) {
builderIt->stateActionRewardVector.back() += ValueType(evaluator.asRational(stateActionReward.getRewardValueExpression()));
}
}
}
}
}
for (auto const& stateProbabilityPair : choice) {
transitionMatrixBuilder.addNextValue(currentRow, stateProbabilityPair.first, stateProbabilityPair.second);
}
++currentRow;
}
// Then, process all labeled choices.
for (auto const& choice : allLabeledChoices) {
std::map<uint_fast64_t, ValueType> stateToRewardMap;
if (commandLabels) {
choiceLabels.get().emplace_back(std::move(choice.getChoiceLabels()));
}
auto builderIt = rewardModelBuilders.begin();
for (auto rewardModelIt = selectedRewardModels.begin(), rewardModelIte = selectedRewardModels.end(); rewardModelIt != rewardModelIte; ++rewardModelIt, ++builderIt) {
if (rewardModelIt->get().hasStateActionRewards()) {
builderIt->stateActionRewardVector.push_back(storm::utility::zero<ValueType>());
for (auto const& stateActionReward : rewardModelIt->get().getStateActionRewards()) {
if (stateActionReward.isLabeled() && stateActionReward.getActionIndex() == choice.getActionIndex()) {
if (evaluator.asBool(stateActionReward.getStatePredicateExpression())) {
builderIt->stateActionRewardVector.back() += ValueType(evaluator.asRational(stateActionReward.getRewardValueExpression()));
}
}
}
}
}
for (auto const& stateProbabilityPair : choice) {
transitionMatrixBuilder.addNextValue(currentRow, stateProbabilityPair.first, stateProbabilityPair.second);
}
++currentRow;
}
}
}
}
return choiceLabels;
}
template <typename ValueType, typename RewardModelType, typename IndexType>
typename ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::ModelComponents ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::buildModelComponents(storm::prism::Program const& program, std::vector<std::reference_wrapper<storm::prism::RewardModel const>> const& selectedRewardModels, Options const& options) {
ModelComponents modelComponents;
uint_fast64_t bitOffset = 0;
VariableInformation variableInformation(program.getManager());
for (auto const& booleanVariable : program.getGlobalBooleanVariables()) {
variableInformation.booleanVariables.emplace_back(booleanVariable.getExpressionVariable(), booleanVariable.getInitialValueExpression().evaluateAsBool(), bitOffset);
++bitOffset;
variableInformation.booleanVariableToIndexMap[booleanVariable.getExpressionVariable()] = variableInformation.booleanVariables.size() - 1;
}
for (auto const& integerVariable : program.getGlobalIntegerVariables()) {
int_fast64_t lowerBound = integerVariable.getLowerBoundExpression().evaluateAsInt();
int_fast64_t upperBound = integerVariable.getUpperBoundExpression().evaluateAsInt();
uint_fast64_t bitwidth = static_cast<uint_fast64_t>(std::ceil(std::log2(upperBound - lowerBound + 1)));
variableInformation.integerVariables.emplace_back(integerVariable.getExpressionVariable(), integerVariable.getInitialValueExpression().evaluateAsInt(), lowerBound, upperBound, bitOffset, bitwidth);
bitOffset += bitwidth;
variableInformation.integerVariableToIndexMap[integerVariable.getExpressionVariable()] = variableInformation.integerVariables.size() - 1;
}
for (auto const& module : program.getModules()) {
for (auto const& booleanVariable : module.getBooleanVariables()) {
variableInformation.booleanVariables.emplace_back(booleanVariable.getExpressionVariable(), booleanVariable.getInitialValueExpression().evaluateAsBool(), bitOffset);
++bitOffset;
variableInformation.booleanVariableToIndexMap[booleanVariable.getExpressionVariable()] = variableInformation.booleanVariables.size() - 1;
}
for (auto const& integerVariable : module.getIntegerVariables()) {
int_fast64_t lowerBound = integerVariable.getLowerBoundExpression().evaluateAsInt();
int_fast64_t upperBound = integerVariable.getUpperBoundExpression().evaluateAsInt();
uint_fast64_t bitwidth = static_cast<uint_fast64_t>(std::ceil(std::log2(upperBound - lowerBound + 1)));
variableInformation.integerVariables.emplace_back(integerVariable.getExpressionVariable(), integerVariable.getInitialValueExpression().evaluateAsInt(), lowerBound, upperBound, bitOffset, bitwidth);
bitOffset += bitwidth;
variableInformation.integerVariableToIndexMap[integerVariable.getExpressionVariable()] = variableInformation.integerVariables.size() - 1;
}
}
// Create the structure for storing the reachable state space.
uint64_t bitsPerState = ((bitOffset / 64) + 1) * 64;
InternalStateInformation internalStateInformation(bitsPerState);
// 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.getModelType() == storm::prism::Program::ModelType::DTMC || program.getModelType() == storm::prism::Program::ModelType::CTMC;
bool discreteTimeModel = program.getModelType() == storm::prism::Program::ModelType::DTMC || program.getModelType() == storm::prism::Program::ModelType::MDP;
// 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(program, variableInformation, selectedRewardModels, internalStateInformation, options.buildCommandLabels, deterministicModel, discreteTimeModel, 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(program, variableInformation, internalStateInformation);
// Finally -- if requested -- build the state information that can be retrieved from the outside.
if (options.buildStateInformation) {
stateInformation = StateInformation(internalStateInformation.reachableStates.size());
for (auto const& bitVectorIndexPair : internalStateInformation.stateStorage) {
stateInformation.get().valuations[bitVectorIndexPair.second] = unpackStateIntoValuation(bitVectorIndexPair.first, variableInformation);
}
}
return modelComponents;
}
template <typename ValueType, typename RewardModelType, typename IndexType>
storm::models::sparse::StateLabeling ExplicitPrismModelBuilder<ValueType, RewardModelType, IndexType>::buildStateLabeling(storm::prism::Program const& program, VariableInformation const& variableInformation, InternalStateInformation const& internalStateInformation) {
storm::expressions::ExpressionEvaluator<ValueType> evaluator(program.getManager());
std::vector<storm::prism::Label> const& labels = program.getLabels();
storm::models::sparse::StateLabeling result(internalStateInformation.reachableStates.size());
// Initialize labeling.
for (auto const& label : labels) {
result.addLabel(label.getName());
}
for (uint_fast64_t index = 0; index < internalStateInformation.reachableStates.size(); index++) {
unpackStateIntoEvaluator(internalStateInformation.reachableStates[index], variableInformation, evaluator);
for (auto const& label : labels) {
// Add label to state, if the corresponding expression is true.
if (evaluator.asBool(label.getStatePredicateExpression())) {
result.addLabelToState(label.getName(), index);
}
}
}
// Also label the initial state with the special label "init".
result.addLabel("init");
for (auto index : internalStateInformation.initialStateIndices) {
result.addLabelToState("init", index);
}
return result;
}
// 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
}
}