#include "ExplicitDFTModelBuilder.h"
#include <map>
#include <storm/exceptions/IllegalArgumentException.h>
#include "storm/models/sparse/MarkovAutomaton.h"
#include "storm/models/sparse/Ctmc.h"
#include "storm/utility/constants.h"
#include "storm/utility/vector.h"
#include "storm/utility/bitoperations.h"
#include "storm/utility/ProgressMeasurement.h"
#include "storm/exceptions/InvalidArgumentException.h"
#include "storm/exceptions/UnexpectedException.h"
#include "storm/settings/SettingsManager.h"
#include "storm/logic/AtomicLabelFormula.h"
#include "storm-dft/settings/modules/FaultTreeSettings.h"
namespace storm {
namespace builder {
template<typename ValueType, typename StateType>
ExplicitDFTModelBuilder<ValueType, StateType>::ModelComponents::ModelComponents() : transitionMatrix(), stateLabeling(), markovianStates(), exitRates(), choiceLabeling() {
// Intentionally left empty.
}
template<typename ValueType, typename StateType>
ExplicitDFTModelBuilder<ValueType, StateType>::MatrixBuilder::MatrixBuilder(bool canHaveNondeterminism) : mappingOffset(0), stateRemapping(), currentRowGroup(0), currentRow(0), canHaveNondeterminism((canHaveNondeterminism)) {
// Create matrix builder
builder = storm::storage::SparseMatrixBuilder<ValueType>(0, 0, 0, false, canHaveNondeterminism, 0);
}
template<typename ValueType, typename StateType>
ExplicitDFTModelBuilder<ValueType, StateType>::ExplicitDFTModelBuilder(storm::storage::DFT<ValueType> const& dft, storm::storage::DFTIndependentSymmetries const& symmetries, std::set<size_t> const& relevantEvents, bool allowDCForRelevantEvents) :
dft(dft),
stateGenerationInfo(std::make_shared<storm::storage::DFTStateGenerationInfo>(dft.buildStateGenerationInfo(symmetries))),
relevantEvents(relevantEvents),
generator(dft, *stateGenerationInfo),
matrixBuilder(!generator.isDeterministicModel()),
stateStorage(dft.stateBitVectorSize()),
explorationQueue(1, 0, 0.9, false)
{
// Set relevant events
this->dft.setRelevantEvents(this->relevantEvents, allowDCForRelevantEvents);
// Mark top level element as relevant
this->dft.getElement(this->dft.getTopLevelIndex())->setRelevance(true);
if (this->relevantEvents.empty()) {
// Only interested in top level event -> introduce unique failed state
this->uniqueFailedState = true;
STORM_LOG_DEBUG("Using unique failed state with id 0.");
}
// Compute independent subtrees
if (dft.topLevelType() == storm::storage::DFTElementType::OR) {
// We only support this for approximation with top level element OR
for (auto const& child : dft.getGate(dft.getTopLevelIndex())->children()) {
// Consider all children of the top level gate
std::vector<size_t> isubdft;
if (child->nrParents() > 1 || child->hasOutgoingDependencies()) {
STORM_LOG_TRACE("child " << child->name() << "does not allow modularisation.");
isubdft.clear();
} else if (dft.isGate(child->id())) {
isubdft = dft.getGate(child->id())->independentSubDft(false);
} else {
STORM_LOG_ASSERT(dft.isBasicElement(child->id()), "Child is no BE.");
if(dft.getBasicElement(child->id())->hasIngoingDependencies()) {
STORM_LOG_TRACE("child " << child->name() << "does not allow modularisation.");
isubdft.clear();
} else {
isubdft = {child->id()};
}
}
if(isubdft.empty()) {
subtreeBEs.clear();
break;
} else {
// Add new independent subtree
std::vector<size_t> subtree;
for (size_t id : isubdft) {
if (dft.isBasicElement(id)) {
subtree.push_back(id);
}
}
subtreeBEs.push_back(subtree);
}
}
}
if (subtreeBEs.empty()) {
// Consider complete tree
std::vector<size_t> subtree;
for (size_t id = 0; id < dft.nrElements(); ++id) {
if (dft.isBasicElement(id)) {
subtree.push_back(id);
}
}
subtreeBEs.push_back(subtree);
}
for (auto tree : subtreeBEs) {
std::stringstream stream;
stream << "Subtree: ";
for (size_t id : tree) {
stream << id << ", ";
}
STORM_LOG_TRACE(stream.str());
}
}
template<typename ValueType, typename StateType>
void ExplicitDFTModelBuilder<ValueType, StateType>::buildModel(size_t iteration, double approximationThreshold, storm::builder::ApproximationHeuristic approximationHeuristic) {
STORM_LOG_TRACE("Generating DFT state space");
usedHeuristic = approximationHeuristic;
if (approximationThreshold > 0 && !this->uniqueFailedState) {
// Approximation requires unique failed states
// TODO lift this restriction
STORM_LOG_WARN("Approximation requires unique failed state. Forcing use of unique failed state.");
this->uniqueFailedState = true;
}
if (iteration < 1) {
// Initialize
switch (usedHeuristic) {
case storm::builder::ApproximationHeuristic::DEPTH:
explorationQueue = storm::storage::BucketPriorityQueue<ExplorationHeuristic>(dft.nrElements()+1, 0, 0.9, false);
break;
case storm::builder::ApproximationHeuristic::PROBABILITY:
explorationQueue = storm::storage::BucketPriorityQueue<ExplorationHeuristic>(200, 0, 0.9, true);
break;
case storm::builder::ApproximationHeuristic::BOUNDDIFFERENCE:
explorationQueue = storm::storage::BucketPriorityQueue<ExplorationHeuristic>(200, 0, 0.9, true);
break;
default:
STORM_LOG_THROW(false, storm::exceptions::IllegalArgumentException, "Heuristic not known.");
}
modelComponents.markovianStates = storm::storage::BitVector(INITIAL_BITVECTOR_SIZE);
if (this->uniqueFailedState) {
// Introduce explicit fail state
storm::generator::StateBehavior<ValueType, StateType> behavior = generator.createMergeFailedState([this] (DFTStatePointer const& state) {
size_t failedStateId = newIndex++;
STORM_LOG_ASSERT(failedStateId == 0, "Unique failed id is not 0.");
matrixBuilder.stateRemapping.push_back(0);
return failedStateId;
} );
matrixBuilder.setRemapping(0);
STORM_LOG_ASSERT(!behavior.empty(), "Behavior is empty.");
matrixBuilder.newRowGroup();
setMarkovian(behavior.begin()->isMarkovian());
// Now add self loop.
// TODO: maybe use general method.
STORM_LOG_ASSERT(behavior.getNumberOfChoices() == 1, "Wrong number of choices for failed state.");
STORM_LOG_ASSERT(behavior.begin()->size() == 1, "Wrong number of transitions for failed state.");
std::pair<StateType, ValueType> stateProbabilityPair = *(behavior.begin()->begin());
STORM_LOG_ASSERT(stateProbabilityPair.first == 0, "No self loop for failed state.");
STORM_LOG_ASSERT(storm::utility::isOne<ValueType>(stateProbabilityPair.second), "Probability for failed state != 1.");
matrixBuilder.addTransition(stateProbabilityPair.first, stateProbabilityPair.second);
matrixBuilder.finishRow();
}
// Build initial state
this->stateStorage.initialStateIndices = generator.getInitialStates(std::bind(&ExplicitDFTModelBuilder::getOrAddStateIndex, this, std::placeholders::_1));
STORM_LOG_ASSERT(stateStorage.initialStateIndices.size() == 1, "Only one initial state assumed.");
initialStateIndex = stateStorage.initialStateIndices[0];
STORM_LOG_TRACE("Initial state: " << initialStateIndex);
// Initialize heuristic values for inital state
STORM_LOG_ASSERT(!statesNotExplored.at(initialStateIndex).second, "Heuristic for initial state is already initialized");
ExplorationHeuristicPointer heuristic;
switch (usedHeuristic) {
case storm::builder::ApproximationHeuristic::DEPTH:
heuristic = std::make_shared<DFTExplorationHeuristicDepth<ValueType>>(initialStateIndex);
break;
case storm::builder::ApproximationHeuristic::PROBABILITY:
heuristic = std::make_shared<DFTExplorationHeuristicProbability<ValueType>>(initialStateIndex);
break;
case storm::builder::ApproximationHeuristic::BOUNDDIFFERENCE:
heuristic = std::make_shared<DFTExplorationHeuristicBoundDifference<ValueType>>(initialStateIndex);
break;
default:
STORM_LOG_THROW(false, storm::exceptions::IllegalArgumentException, "Heuristic not known.");
}
heuristic->markExpand();
statesNotExplored[initialStateIndex].second = heuristic;
explorationQueue.push(heuristic);
} else {
initializeNextIteration();
}
if (approximationThreshold > 0.0) {
switch (usedHeuristic) {
case storm::builder::ApproximationHeuristic::DEPTH:
approximationThreshold = iteration + 1;
break;
case storm::builder::ApproximationHeuristic::PROBABILITY:
case storm::builder::ApproximationHeuristic::BOUNDDIFFERENCE:
approximationThreshold = std::pow(2, -(double)iteration); // Need conversion to avoid overflow when negating
break;
default:
STORM_LOG_THROW(false, storm::exceptions::IllegalArgumentException, "Heuristic not known.");
}
}
exploreStateSpace(approximationThreshold);
size_t stateSize = stateStorage.getNumberOfStates() + (this->uniqueFailedState ? 1 : 0);
modelComponents.markovianStates.resize(stateSize);
modelComponents.deterministicModel = generator.isDeterministicModel();
// Fix the entries in the transition matrix according to the mapping of ids to row group indices
STORM_LOG_ASSERT(matrixBuilder.getRemapping(initialStateIndex) == initialStateIndex, "Initial state should not be remapped.");
// TODO: do not consider all rows?
STORM_LOG_TRACE("Remap matrix: " << matrixBuilder.stateRemapping << ", offset: " << matrixBuilder.mappingOffset);
matrixBuilder.remap();
STORM_LOG_TRACE("State remapping: " << matrixBuilder.stateRemapping);
STORM_LOG_TRACE("Markovian states: " << modelComponents.markovianStates);
STORM_LOG_DEBUG("Model has " << stateSize << " states");
STORM_LOG_DEBUG("Model is " << (generator.isDeterministicModel() ? "deterministic" : "non-deterministic"));
// Build transition matrix
modelComponents.transitionMatrix = matrixBuilder.builder.build(stateSize, stateSize);
if (stateSize <= 15) {
STORM_LOG_TRACE("Transition matrix: " << std::endl << modelComponents.transitionMatrix);
} else {
STORM_LOG_TRACE("Transition matrix: too big to print");
}
buildLabeling();
}
template<typename ValueType, typename StateType>
void ExplicitDFTModelBuilder<ValueType, StateType>::initializeNextIteration() {
STORM_LOG_TRACE("Refining DFT state space");
// TODO: should be easier now as skipped states all are at the end of the matrix
// Push skipped states to explore queue
// TODO: remove
for (auto const& skippedState : skippedStates) {
statesNotExplored[skippedState.second.first->getId()] = skippedState.second;
explorationQueue.push(skippedState.second.second);
}
// Initialize matrix builder again
// TODO: avoid copy
std::vector<uint_fast64_t> copyRemapping = matrixBuilder.stateRemapping;
matrixBuilder = MatrixBuilder(!generator.isDeterministicModel());
matrixBuilder.stateRemapping = copyRemapping;
StateType nrStates = modelComponents.transitionMatrix.getRowGroupCount();
STORM_LOG_ASSERT(nrStates == matrixBuilder.stateRemapping.size(), "No. of states does not coincide with mapping size.");
// Start by creating a remapping from the old indices to the new indices
std::vector<StateType> indexRemapping = std::vector<StateType>(nrStates, 0);
auto iterSkipped = skippedStates.begin();
size_t skippedBefore = 0;
for (size_t i = 0; i < indexRemapping.size(); ++i) {
while (iterSkipped != skippedStates.end() && iterSkipped->first <= i) {
STORM_LOG_ASSERT(iterSkipped->first < indexRemapping.size(), "Skipped is too high.");
++skippedBefore;
++iterSkipped;
}
indexRemapping[i] = i - skippedBefore;
}
// Set remapping
size_t nrExpandedStates = nrStates - skippedBefore;
matrixBuilder.mappingOffset = nrStates;
STORM_LOG_TRACE("# expanded states: " << nrExpandedStates);
StateType skippedIndex = nrExpandedStates;
std::map<StateType, std::pair<DFTStatePointer, ExplorationHeuristicPointer>> skippedStatesNew;
for (size_t id = 0; id < matrixBuilder.stateRemapping.size(); ++id) {
StateType index = matrixBuilder.getRemapping(id);
auto itFind = skippedStates.find(index);
if (itFind != skippedStates.end()) {
// Set new mapping for skipped state
matrixBuilder.stateRemapping[id] = skippedIndex;
skippedStatesNew[skippedIndex] = itFind->second;
indexRemapping[index] = skippedIndex;
++skippedIndex;
} else {
// Set new mapping for expanded state
matrixBuilder.stateRemapping[id] = indexRemapping[index];
}
}
STORM_LOG_TRACE("New state remapping: " << matrixBuilder.stateRemapping);
std::stringstream ss;
ss << "Index remapping:" << std::endl;
for (auto tmp : indexRemapping) {
ss << tmp << " ";
}
STORM_LOG_TRACE(ss.str());
// Remap markovian states
storm::storage::BitVector markovianStatesNew = storm::storage::BitVector(modelComponents.markovianStates.size(), true);
// Iterate over all not set bits
modelComponents.markovianStates.complement();
size_t index = modelComponents.markovianStates.getNextSetIndex(0);
while (index < modelComponents.markovianStates.size()) {
markovianStatesNew.set(indexRemapping[index], false);
index = modelComponents.markovianStates.getNextSetIndex(index+1);
}
STORM_LOG_ASSERT(modelComponents.markovianStates.size() - modelComponents.markovianStates.getNumberOfSetBits() == markovianStatesNew.getNumberOfSetBits(), "Remapping of markovian states is wrong.");
STORM_LOG_ASSERT(markovianStatesNew.size() == nrStates, "No. of states does not coincide with markovian size.");
modelComponents.markovianStates = markovianStatesNew;
// Build submatrix for expanded states
// TODO: only use row groups when necessary
for (StateType oldRowGroup = 0; oldRowGroup < modelComponents.transitionMatrix.getRowGroupCount(); ++oldRowGroup) {
if (indexRemapping[oldRowGroup] < nrExpandedStates) {
// State is expanded -> copy to new matrix
matrixBuilder.newRowGroup();
for (StateType oldRow = modelComponents.transitionMatrix.getRowGroupIndices()[oldRowGroup]; oldRow < modelComponents.transitionMatrix.getRowGroupIndices()[oldRowGroup+1]; ++oldRow) {
for (typename storm::storage::SparseMatrix<ValueType>::const_iterator itEntry = modelComponents.transitionMatrix.begin(oldRow); itEntry != modelComponents.transitionMatrix.end(oldRow); ++itEntry) {
auto itFind = skippedStates.find(itEntry->getColumn());
if (itFind != skippedStates.end()) {
// Set id for skipped states as we remap it later
matrixBuilder.addTransition(matrixBuilder.mappingOffset + itFind->second.first->getId(), itEntry->getValue());
} else {
// Set newly remapped index for expanded states
matrixBuilder.addTransition(indexRemapping[itEntry->getColumn()], itEntry->getValue());
}
}
matrixBuilder.finishRow();
}
}
}
skippedStates = skippedStatesNew;
STORM_LOG_ASSERT(matrixBuilder.getCurrentRowGroup() == nrExpandedStates, "Row group size does not match.");
skippedStates.clear();
}
template<typename ValueType, typename StateType>
void ExplicitDFTModelBuilder<ValueType, StateType>::exploreStateSpace(double approximationThreshold) {
size_t nrExpandedStates = 0;
size_t nrSkippedStates = 0;
storm::utility::ProgressMeasurement progress("explored states");
progress.startNewMeasurement(0);
// TODO: do not empty queue every time but break before
while (!explorationQueue.empty()) {
// Get the first state in the queue
ExplorationHeuristicPointer currentExplorationHeuristic = explorationQueue.pop();
StateType currentId = currentExplorationHeuristic->getId();
auto itFind = statesNotExplored.find(currentId);
STORM_LOG_ASSERT(itFind != statesNotExplored.end(), "Id " << currentId << " not found");
DFTStatePointer currentState = itFind->second.first;
STORM_LOG_ASSERT(currentExplorationHeuristic == itFind->second.second, "Exploration heuristics do not match");
STORM_LOG_ASSERT(currentState->getId() == currentId, "Ids do not match");
// Remove it from the list of not explored states
statesNotExplored.erase(itFind);
STORM_LOG_ASSERT(stateStorage.stateToId.contains(currentState->status()), "State is not contained in state storage.");
STORM_LOG_ASSERT(stateStorage.stateToId.getValue(currentState->status()) == currentId, "Ids of states do not coincide.");
// Get concrete state if necessary
if (currentState->isPseudoState()) {
// Create concrete state from pseudo state
currentState->construct();
}
STORM_LOG_ASSERT(!currentState->isPseudoState(), "State is pseudo state.");
// Remember that the current row group was actually filled with the transitions of a different state
matrixBuilder.setRemapping(currentId);
matrixBuilder.newRowGroup();
// Try to explore the next state
generator.load(currentState);
//if (approximationThreshold > 0.0 && nrExpandedStates > approximationThreshold && !currentExplorationHeuristic->isExpand()) {
if (approximationThreshold > 0.0 && currentExplorationHeuristic->isSkip(approximationThreshold)) {
// Skip the current state
++nrSkippedStates;
STORM_LOG_TRACE("Skip expansion of state: " << dft.getStateString(currentState));
setMarkovian(true);
// Add transition to target state with temporary value 0
// TODO: what to do when there is no unique target state?
STORM_LOG_ASSERT(this->uniqueFailedState, "Approximation only works with unique failed state");
matrixBuilder.addTransition(0, storm::utility::zero<ValueType>());
// Remember skipped state
skippedStates[matrixBuilder.getCurrentRowGroup() - 1] = std::make_pair(currentState, currentExplorationHeuristic);
matrixBuilder.finishRow();
} else {
// Explore the current state
++nrExpandedStates;
storm::generator::StateBehavior<ValueType, StateType> behavior = generator.expand(std::bind(&ExplicitDFTModelBuilder::getOrAddStateIndex, this, std::placeholders::_1));
STORM_LOG_ASSERT(!behavior.empty(), "Behavior is empty.");
setMarkovian(behavior.begin()->isMarkovian());
// Now add all choices.
for (auto const& choice : behavior) {
// Add the probabilistic behavior to the matrix.
for (auto const& stateProbabilityPair : choice) {
STORM_LOG_ASSERT(!storm::utility::isZero(stateProbabilityPair.second), "Probability zero.");
// Set transition to state id + offset. This helps in only remapping all previously skipped states.
matrixBuilder.addTransition(matrixBuilder.mappingOffset + stateProbabilityPair.first, stateProbabilityPair.second);
// Set heuristic values for reached states
auto iter = statesNotExplored.find(stateProbabilityPair.first);
if (iter != statesNotExplored.end()) {
// Update heuristic values
DFTStatePointer state = iter->second.first;
if (!iter->second.second) {
// Initialize heuristic values
ExplorationHeuristicPointer heuristic;
switch (usedHeuristic) {
case storm::builder::ApproximationHeuristic::DEPTH:
heuristic = std::make_shared<DFTExplorationHeuristicDepth<ValueType>>(stateProbabilityPair.first, *currentExplorationHeuristic, stateProbabilityPair.second, choice.getTotalMass());
break;
case storm::builder::ApproximationHeuristic::PROBABILITY:
heuristic = std::make_shared<DFTExplorationHeuristicProbability<ValueType>>(stateProbabilityPair.first, *currentExplorationHeuristic, stateProbabilityPair.second, choice.getTotalMass());
break;
case storm::builder::ApproximationHeuristic::BOUNDDIFFERENCE:
heuristic = std::make_shared<DFTExplorationHeuristicBoundDifference<ValueType>>(stateProbabilityPair.first, *currentExplorationHeuristic, stateProbabilityPair.second, choice.getTotalMass());
break;
default:
STORM_LOG_THROW(false, storm::exceptions::IllegalArgumentException, "Heuristic not known.");
}
iter->second.second = heuristic;
if (state->hasFailed(dft.getTopLevelIndex()) || state->isFailsafe(dft.getTopLevelIndex()) || state->getFailableElements().hasDependencies() || (!state->getFailableElements().hasDependencies() && !state->getFailableElements().hasBEs())) {
// Do not skip absorbing state or if reached by dependencies
iter->second.second->markExpand();
}
if (usedHeuristic == storm::builder::ApproximationHeuristic::BOUNDDIFFERENCE) {
// Compute bounds for heuristic now
if (state->isPseudoState()) {
// Create concrete state from pseudo state
state->construct();
}
STORM_LOG_ASSERT(!currentState->isPseudoState(), "State is pseudo state.");
// Initialize bounds
// TODO: avoid hack
ValueType lowerBound = getLowerBound(state);
ValueType upperBound = getUpperBound(state);
heuristic->setBounds(lowerBound, upperBound);
}
explorationQueue.push(heuristic);
} else if (!iter->second.second->isExpand()) {
double oldPriority = iter->second.second->getPriority();
if (iter->second.second->updateHeuristicValues(*currentExplorationHeuristic, stateProbabilityPair.second, choice.getTotalMass())) {
// Update priority queue
explorationQueue.update(iter->second.second, oldPriority);
}
}
}
}
matrixBuilder.finishRow();
}
}
// Output number of currently explored states
if (nrExpandedStates % 100 == 0) {
progress.updateProgress(nrExpandedStates);
}
} // end exploration
STORM_LOG_INFO("Expanded " << nrExpandedStates << " states");
STORM_LOG_INFO("Skipped " << nrSkippedStates << " states");
STORM_LOG_ASSERT(nrSkippedStates == skippedStates.size(), "Nr skipped states is wrong");
}
template<typename ValueType, typename StateType>
void ExplicitDFTModelBuilder<ValueType, StateType>::buildLabeling() {
// Build state labeling
modelComponents.stateLabeling = storm::models::sparse::StateLabeling(modelComponents.transitionMatrix.getRowGroupCount());
// Initial state
modelComponents.stateLabeling.addLabel("init");
STORM_LOG_ASSERT(matrixBuilder.getRemapping(initialStateIndex) == initialStateIndex, "Initial state should not be remapped.");
modelComponents.stateLabeling.addLabelToState("init", initialStateIndex);
// Label all states corresponding to their status (failed, failed/dont care BE)
modelComponents.stateLabeling.addLabel("failed");
// Collect labels for all necessary elements
for (size_t id = 0; id < dft.nrElements(); ++id) {
std::shared_ptr<storage::DFTElement<ValueType> const> element = dft.getElement(id);
if (element->isRelevant()) {
modelComponents.stateLabeling.addLabel(element->name() + "_fail");
modelComponents.stateLabeling.addLabel(element->name() + "_dc");
}
}
// Set labels to states
if (this->uniqueFailedState) {
modelComponents.stateLabeling.addLabelToState("failed", 0);
}
for (auto const& stateIdPair : stateStorage.stateToId) {
storm::storage::BitVector state = stateIdPair.first;
size_t stateId = matrixBuilder.getRemapping(stateIdPair.second);
if (dft.hasFailed(state, *stateGenerationInfo)) {
modelComponents.stateLabeling.addLabelToState("failed", stateId);
}
// Set fail/dont care status for each necessary element
for (size_t id = 0; id < dft.nrElements(); ++id) {
std::shared_ptr<storage::DFTElement<ValueType> const> element = dft.getElement(id);
if (element->isRelevant()){
storm::storage::DFTElementState elementState = storm::storage::DFTState<ValueType>::getElementState(state, *stateGenerationInfo, element->id());
switch (elementState) {
case storm::storage::DFTElementState::Failed:
modelComponents.stateLabeling.addLabelToState(element->name() + "_fail", stateId);
break;
case storm::storage::DFTElementState::DontCare:
modelComponents.stateLabeling.addLabelToState(element->name() + "_dc", stateId);
break;
case storm::storage::DFTElementState::Operational:
case storm::storage::DFTElementState::Failsafe:
// do nothing
break;
default:
STORM_LOG_ASSERT(false, "Unknown element state " << elementState);
}
}
}
}
STORM_LOG_TRACE(modelComponents.stateLabeling);
}
template<typename ValueType, typename StateType>
std::shared_ptr<storm::models::sparse::Model<ValueType>> ExplicitDFTModelBuilder<ValueType, StateType>::getModel() {
STORM_LOG_ASSERT(skippedStates.size() == 0, "Concrete model has skipped states");
return createModel(false);
}
template<typename ValueType, typename StateType>
std::shared_ptr<storm::models::sparse::Model<ValueType>> ExplicitDFTModelBuilder<ValueType, StateType>::getModelApproximation(bool lowerBound, bool expectedTime) {
if (expectedTime) {
// TODO: handle case with no skipped states
changeMatrixBound(modelComponents.transitionMatrix, lowerBound);
return createModel(true);
} else {
// Change model for probabilities
// TODO: make nicer
storm::storage::SparseMatrix<ValueType> matrix = modelComponents.transitionMatrix;
storm::models::sparse::StateLabeling labeling = modelComponents.stateLabeling;
if (lowerBound) {
// Set self loop for lower bound
for (auto it = skippedStates.begin(); it != skippedStates.end(); ++it) {
auto matrixEntry = matrix.getRow(it->first, 0).begin();
STORM_LOG_ASSERT(matrixEntry->getColumn() == 0, "Transition has wrong target state.");
STORM_LOG_ASSERT(!it->second.first->isPseudoState(), "State is still pseudo state.");
matrixEntry->setValue(storm::utility::one<ValueType>());
matrixEntry->setColumn(it->first);
}
} else {
// Make skipped states failed states for upper bound
storm::storage::BitVector failedStates = modelComponents.stateLabeling.getStates("failed");
for (auto it = skippedStates.begin(); it != skippedStates.end(); ++it) {
failedStates.set(it->first);
}
labeling.setStates("failed", failedStates);
}
std::shared_ptr<storm::models::sparse::Model<ValueType>> model;
if (modelComponents.deterministicModel) {
model = std::make_shared<storm::models::sparse::Ctmc<ValueType>>(std::move(matrix), std::move(labeling));
} else {
// Build MA
// Compute exit rates
// TODO: avoid computing multiple times
modelComponents.exitRates = std::vector<ValueType>(modelComponents.markovianStates.size());
std::vector<typename storm::storage::SparseMatrix<ValueType>::index_type> indices = matrix.getRowGroupIndices();
for (StateType stateIndex = 0; stateIndex < modelComponents.markovianStates.size(); ++stateIndex) {
if (modelComponents.markovianStates[stateIndex]) {
modelComponents.exitRates[stateIndex] = matrix.getRowSum(indices[stateIndex]);
} else {
modelComponents.exitRates[stateIndex] = storm::utility::zero<ValueType>();
}
}
STORM_LOG_TRACE("Exit rates: " << modelComponents.exitRates);
storm::storage::sparse::ModelComponents<ValueType> maComponents(std::move(matrix), std::move(labeling));
maComponents.rateTransitions = true;
maComponents.markovianStates = modelComponents.markovianStates;
maComponents.exitRates = modelComponents.exitRates;
std::shared_ptr<storm::models::sparse::MarkovAutomaton<ValueType>> ma = std::make_shared<storm::models::sparse::MarkovAutomaton<ValueType>>(std::move(maComponents));
if (ma->hasOnlyTrivialNondeterminism()) {
// Markov automaton can be converted into CTMC
// TODO: change components which were not moved accordingly
model = ma->convertToCtmc();
} else {
model = ma;
}
}
if (model->getNumberOfStates() <= 15) {
STORM_LOG_TRACE("Transition matrix: " << std::endl << model->getTransitionMatrix());
} else {
STORM_LOG_TRACE("Transition matrix: too big to print");
}
return model;
}
}
template<typename ValueType, typename StateType>
std::shared_ptr<storm::models::sparse::Model<ValueType>> ExplicitDFTModelBuilder<ValueType, StateType>::createModel(bool copy) {
std::shared_ptr<storm::models::sparse::Model<ValueType>> model;
if (modelComponents.deterministicModel) {
// Build CTMC
if (copy) {
model = std::make_shared<storm::models::sparse::Ctmc<ValueType>>(modelComponents.transitionMatrix, modelComponents.stateLabeling);
} else {
model = std::make_shared<storm::models::sparse::Ctmc<ValueType>>(std::move(modelComponents.transitionMatrix), std::move(modelComponents.stateLabeling));
}
} else {
// Build MA
// Compute exit rates
// TODO: avoid computing multiple times
modelComponents.exitRates = std::vector<ValueType>(modelComponents.markovianStates.size());
std::vector<typename storm::storage::SparseMatrix<ValueType>::index_type> indices = modelComponents.transitionMatrix.getRowGroupIndices();
for (StateType stateIndex = 0; stateIndex < modelComponents.markovianStates.size(); ++stateIndex) {
if (modelComponents.markovianStates[stateIndex]) {
modelComponents.exitRates[stateIndex] = modelComponents.transitionMatrix.getRowSum(indices[stateIndex]);
} else {
modelComponents.exitRates[stateIndex] = storm::utility::zero<ValueType>();
}
}
STORM_LOG_TRACE("Exit rates: " << modelComponents.exitRates);
std::shared_ptr<storm::models::sparse::MarkovAutomaton<ValueType>> ma;
if (copy) {
storm::storage::sparse::ModelComponents<ValueType> maComponents(modelComponents.transitionMatrix, modelComponents.stateLabeling);
maComponents.rateTransitions = true;
maComponents.markovianStates = modelComponents.markovianStates;
maComponents.exitRates = modelComponents.exitRates;
ma = std::make_shared<storm::models::sparse::MarkovAutomaton<ValueType>>(std::move(maComponents));
} else {
storm::storage::sparse::ModelComponents<ValueType> maComponents(std::move(modelComponents.transitionMatrix), std::move(modelComponents.stateLabeling));
maComponents.rateTransitions = true;
maComponents.markovianStates = std::move(modelComponents.markovianStates);
maComponents.exitRates = std::move(modelComponents.exitRates);
ma = std::make_shared<storm::models::sparse::MarkovAutomaton<ValueType>>(std::move(maComponents));
}
if (ma->hasOnlyTrivialNondeterminism()) {
// Markov automaton can be converted into CTMC
// TODO: change components which were not moved accordingly
model = ma->convertToCtmc();
} else {
model = ma;
}
}
if (model->getNumberOfStates() <= 15) {
STORM_LOG_TRACE("Transition matrix: " << std::endl << model->getTransitionMatrix());
} else {
STORM_LOG_TRACE("Transition matrix: too big to print");
}
return model;
}
template<typename ValueType, typename StateType>
void ExplicitDFTModelBuilder<ValueType, StateType>::changeMatrixBound(storm::storage::SparseMatrix<ValueType> & matrix, bool lowerBound) const {
// Set lower bound for skipped states
for (auto it = skippedStates.begin(); it != skippedStates.end(); ++it) {
auto matrixEntry = matrix.getRow(it->first, 0).begin();
STORM_LOG_ASSERT(matrixEntry->getColumn() == 0, "Transition has wrong target state.");
STORM_LOG_ASSERT(!it->second.first->isPseudoState(), "State is still pseudo state.");
ExplorationHeuristicPointer heuristic = it->second.second;
if (storm::utility::isInfinity(heuristic->getUpperBound())) {
// Initialize bounds
ValueType lowerBound = getLowerBound(it->second.first);
ValueType upperBound = getUpperBound(it->second.first);
heuristic->setBounds(lowerBound, upperBound);
}
// Change bound
if (lowerBound) {
matrixEntry->setValue(it->second.second->getLowerBound());
} else {
matrixEntry->setValue(it->second.second->getUpperBound());
}
}
}
template<typename ValueType, typename StateType>
ValueType ExplicitDFTModelBuilder<ValueType, StateType>::getLowerBound(DFTStatePointer const& state) const {
// Get the lower bound by considering the failure of all possible BEs
ValueType lowerBound = storm::utility::zero<ValueType>();
for (state->getFailableElements().init(false); !state->getFailableElements().isEnd(); state->getFailableElements().next()) {
lowerBound += state->getBERate(state->getFailableElements().get());
}
STORM_LOG_TRACE("Lower bound is " << lowerBound << " for state " << state->getId());
return lowerBound;
}
template<typename ValueType, typename StateType>
ValueType ExplicitDFTModelBuilder<ValueType, StateType>::getUpperBound(DFTStatePointer const& state) const {
if (state->hasFailed(dft.getTopLevelIndex())) {
return storm::utility::zero<ValueType>();
}
// Get the upper bound by considering the failure of all BEs
ValueType upperBound = storm::utility::one<ValueType>();
ValueType rateSum = storm::utility::zero<ValueType>();
// Compute for each independent subtree
for (std::vector<size_t> const& subtree : subtreeBEs) {
// Get all possible rates
std::vector<ValueType> rates;
storm::storage::BitVector coldBEs(subtree.size(), false);
for (size_t i = 0; i < subtree.size(); ++i) {
size_t id = subtree[i];
if (state->isOperational(id)) {
// Get BE rate
ValueType rate = state->getBERate(id);
if (storm::utility::isZero<ValueType>(rate)) {
// Get active failure rate for cold BE
auto be = dft.getBasicElement(id);
switch (be->type()) {
case storm::storage::DFTElementType::BE_EXP:
{
auto beExp = std::static_pointer_cast<storm::storage::BEExponential<ValueType> const>(be);
rate = beExp->activeFailureRate();
STORM_LOG_ASSERT(!storm::utility::isZero<ValueType>(rate), "Failure rate should not be zero.");
// Mark BE as cold
coldBEs.set(i, true);
break;
}
case storm::storage::DFTElementType::BE_CONST:
{
// Ignore BE which cannot fail
continue;
}
default:
STORM_LOG_THROW(false, storm::exceptions::InvalidArgumentException, "BE of type '" << be->type() << "' is not known.");
break;
}
}
rates.push_back(rate);
rateSum += rate;
}
}
STORM_LOG_ASSERT(rates.size() > 0, "No rates failable");
// Sort rates
std::sort(rates.begin(), rates.end());
std::vector<size_t> rateCount(rates.size(), 0);
size_t lastIndex = 0;
for (size_t i = 0; i < rates.size(); ++i) {
if (rates[lastIndex] != rates[i]) {
lastIndex = i;
}
++rateCount[lastIndex];
}
for (size_t i = 0; i < rates.size(); ++i) {
// Cold BEs cannot fail in the first step
if (!coldBEs.get(i) && rateCount[i] > 0) {
std::iter_swap(rates.begin() + i, rates.end() - 1);
// Compute AND MTTF of subtree without current rate and scale with current rate
upperBound += rates.back() * rateCount[i] * computeMTTFAnd(rates, rates.size() - 1);
// Swap back
// TODO try to avoid swapping back
std::iter_swap(rates.begin() + i, rates.end() - 1);
}
}
}
STORM_LOG_TRACE("Upper bound is " << (rateSum / upperBound) << " for state " << state->getId());
STORM_LOG_ASSERT(!storm::utility::isZero(upperBound), "Upper bound is 0");
STORM_LOG_ASSERT(!storm::utility::isZero(rateSum), "State is absorbing");
return rateSum / upperBound;
}
template<typename ValueType, typename StateType>
ValueType ExplicitDFTModelBuilder<ValueType, StateType>::computeMTTFAnd(std::vector<ValueType> const& rates, size_t size) const {
ValueType result = storm::utility::zero<ValueType>();
if (size == 0) {
return result;
}
// TODO: works only for <64 BEs!
// WARNING: this code produces wrong results for more than 32 BEs
/*for (size_t i = 1; i < 4 && i <= rates.size(); ++i) {
size_t permutation = smallestIntWithNBitsSet(static_cast<size_t>(i));
ValueType sum = storm::utility::zero<ValueType>();
do {
ValueType permResult = storm::utility::zero<ValueType>();
for(size_t j = 0; j < rates.size(); ++j) {
if(permutation & static_cast<size_t>(1 << static_cast<size_t>(j))) {
// WARNING: if the first bit is set, it also recognizes the 32nd bit as set
// TODO: fix
permResult += rates[j];
}
}
permutation = nextBitPermutation(permutation);
STORM_LOG_ASSERT(!storm::utility::isZero(permResult), "PermResult is 0");
sum += storm::utility::one<ValueType>() / permResult;
} while(permutation < (static_cast<size_t>(1) << rates.size()) && permutation != 0);
if (i % 2 == 0) {
result -= sum;
} else {
result += sum;
}
}*/
// Compute result with permutations of size <= 3
ValueType one = storm::utility::one<ValueType>();
for (size_t i1 = 0; i1 < size; ++i1) {
// + 1/a
ValueType sum = rates[i1];
result += one / sum;
for (size_t i2 = 0; i2 < i1; ++i2) {
// - 1/(a+b)
ValueType sum2 = sum + rates[i2];
result -= one / sum2;
for (size_t i3 = 0; i3 < i2; ++i3) {
// + 1/(a+b+c)
result += one / (sum2 + rates[i3]);
}
}
}
STORM_LOG_ASSERT(!storm::utility::isZero(result), "UpperBound is 0");
return result;
}
template<typename ValueType, typename StateType>
StateType ExplicitDFTModelBuilder<ValueType, StateType>::getOrAddStateIndex(DFTStatePointer const& state) {
StateType stateId;
bool changed = false;
if (stateGenerationInfo->hasSymmetries()) {
// Order state by symmetry
STORM_LOG_TRACE("Check for symmetry: " << dft.getStateString(state));
changed = state->orderBySymmetry();
STORM_LOG_TRACE("State " << (changed ? "changed to " : "did not change") << (changed ? dft.getStateString(state) : ""));
}
if (stateStorage.stateToId.contains(state->status())) {
// State already exists
stateId = stateStorage.stateToId.getValue(state->status());
STORM_LOG_TRACE("State " << dft.getStateString(state) << " with id " << stateId << " already exists");
if (!changed) {
// Check if state is pseudo state
// If state is explored already the possible pseudo state was already constructed
auto iter = statesNotExplored.find(stateId);
if (iter != statesNotExplored.end() && iter->second.first->isPseudoState()) {
// Create pseudo state now
STORM_LOG_ASSERT(iter->second.first->getId() == stateId, "Ids do not match.");
STORM_LOG_ASSERT(iter->second.first->status() == state->status(), "Pseudo states do not coincide.");
state->setId(stateId);
// Update mapping to map to concrete state now
// TODO: just change pointer?
statesNotExplored[stateId] = std::make_pair(state, iter->second.second);
// We do not push the new state on the exploration queue as the pseudo state was already pushed
STORM_LOG_TRACE("Created pseudo state " << dft.getStateString(state));
}
}
} else {
// State does not exist yet
STORM_LOG_ASSERT(state->isPseudoState() == changed, "State type (pseudo/concrete) wrong.");
// Create new state
state->setId(newIndex++);
stateId = stateStorage.stateToId.findOrAdd(state->status(), state->getId());
STORM_LOG_ASSERT(stateId == state->getId(), "Ids do not match.");
// Insert state as not yet explored
ExplorationHeuristicPointer nullHeuristic;
statesNotExplored[stateId] = std::make_pair(state, nullHeuristic);
// Reserve one slot for the new state in the remapping
matrixBuilder.stateRemapping.push_back(0);
STORM_LOG_TRACE("New " << (state->isPseudoState() ? "pseudo" : "concrete") << " state: " << dft.getStateString(state));
}
return stateId;
}
template<typename ValueType, typename StateType>
void ExplicitDFTModelBuilder<ValueType, StateType>::setMarkovian(bool markovian) {
if (matrixBuilder.getCurrentRowGroup() > modelComponents.markovianStates.size()) {
// Resize BitVector
modelComponents.markovianStates.resize(modelComponents.markovianStates.size() + INITIAL_BITVECTOR_SIZE);
}
modelComponents.markovianStates.set(matrixBuilder.getCurrentRowGroup() - 1, markovian);
}
template<typename ValueType, typename StateType>
void ExplicitDFTModelBuilder<ValueType, StateType>::printNotExplored() const {
std::cout << "states not explored:" << std::endl;
for (auto it : statesNotExplored) {
std::cout << it.first << " -> " << dft.getStateString(it.second.first) << std::endl;
}
}
// Explicitly instantiate the class.
template class ExplicitDFTModelBuilder<double>;
#ifdef STORM_HAVE_CARL
template class ExplicitDFTModelBuilder<storm::RationalFunction>;
#endif
} // namespace builder
} // namespace storm