#include "src/modelchecker/csl/SparseCtmcCslModelChecker.h"
#include <vector>
#include "src/utility/macros.h"
#include "src/utility/vector.h"
#include "src/utility/graph.h"
#include "src/utility/solver.h"
#include "src/utility/numerical.h"
#include "src/modelchecker/prctl/SparseDtmcPrctlModelChecker.h"
#include "src/modelchecker/results/ExplicitQualitativeCheckResult.h"
#include "src/modelchecker/results/ExplicitQuantitativeCheckResult.h"
#include "src/exceptions/InvalidStateException.h"
#include "src/exceptions/InvalidPropertyException.h"
#include "src/exceptions/NotImplementedException.h"
namespace storm {
namespace modelchecker {
template<class ValueType>
SparseCtmcCslModelChecker<ValueType>::SparseCtmcCslModelChecker(storm::models::sparse::Ctmc<ValueType> const& model) : SparsePropositionalModelChecker<ValueType>(model), linearEquationSolverFactory(new storm::utility::solver::LinearEquationSolverFactory<ValueType>()) {
// Intentionally left empty.
}
template<class ValueType>
SparseCtmcCslModelChecker<ValueType>::SparseCtmcCslModelChecker(storm::models::sparse::Ctmc<ValueType> const& model, std::unique_ptr<storm::utility::solver::LinearEquationSolverFactory<ValueType>>&& linearEquationSolverFactory) : SparsePropositionalModelChecker<ValueType>(model), linearEquationSolverFactory(std::move(linearEquationSolverFactory)) {
// Intentionally left empty.
}
template<class ValueType>
bool SparseCtmcCslModelChecker<ValueType>::canHandle(storm::logic::Formula const& formula) const {
return formula.isCslStateFormula() || formula.isCslPathFormula() || formula.isRewardPathFormula();
}
template<class ValueType>
std::unique_ptr<CheckResult> SparseCtmcCslModelChecker<ValueType>::computeBoundedUntilProbabilities(storm::logic::BoundedUntilFormula const& pathFormula, bool qualitative, boost::optional<storm::logic::OptimalityType> const& optimalityType) {
std::unique_ptr<CheckResult> leftResultPointer = this->check(pathFormula.getLeftSubformula());
std::unique_ptr<CheckResult> rightResultPointer = this->check(pathFormula.getRightSubformula());
ExplicitQualitativeCheckResult const& leftResult = leftResultPointer->asExplicitQualitativeCheckResult();;
ExplicitQualitativeCheckResult const& rightResult = rightResultPointer->asExplicitQualitativeCheckResult();
double lowerBound = 0;
double upperBound = 0;
if (!pathFormula.hasDiscreteTimeBound()) {
std::pair<double, double> const& intervalBounds = pathFormula.getIntervalBounds();
lowerBound = intervalBounds.first;
upperBound = intervalBounds.second;
} else {
upperBound = pathFormula.getDiscreteTimeBound();
}
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(this->computeBoundedUntilProbabilitiesHelper(leftResult.getTruthValuesVector(), rightResult.getTruthValuesVector(), this->getModel().getExitRateVector(), qualitative, lowerBound, upperBound)));
}
template<class ValueType>
std::unique_ptr<CheckResult> SparseCtmcCslModelChecker<ValueType>::computeNextProbabilities(storm::logic::NextFormula const& pathFormula, bool qualitative, boost::optional<storm::logic::OptimalityType> const& optimalityType) {
std::unique_ptr<CheckResult> subResultPointer = this->check(pathFormula.getSubformula());
ExplicitQualitativeCheckResult const& subResult = subResultPointer->asExplicitQualitativeCheckResult();
std::vector<ValueType> result = SparseDtmcPrctlModelChecker<ValueType>::computeNextProbabilitiesHelper(this->computeProbabilityMatrix(this->getModel().getTransitionMatrix(), this->getModel().getExitRateVector()), subResult.getTruthValuesVector(), *this->linearEquationSolverFactory);
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(std::move(result)));
}
template<class ValueType>
std::unique_ptr<CheckResult> SparseCtmcCslModelChecker<ValueType>::computeUntilProbabilities(storm::logic::UntilFormula const& pathFormula, bool qualitative, boost::optional<storm::logic::OptimalityType> const& optimalityType) {
std::unique_ptr<CheckResult> leftResultPointer = this->check(pathFormula.getLeftSubformula());
std::unique_ptr<CheckResult> rightResultPointer = this->check(pathFormula.getRightSubformula());
ExplicitQualitativeCheckResult const& leftResult = leftResultPointer->asExplicitQualitativeCheckResult();
ExplicitQualitativeCheckResult const& rightResult = rightResultPointer->asExplicitQualitativeCheckResult();
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(this->computeUntilProbabilitiesHelper(this->computeProbabilityMatrix(this->getModel().getTransitionMatrix(), this->getModel().getExitRateVector()), this->getModel().getBackwardTransitions(), leftResult.getTruthValuesVector(), rightResult.getTruthValuesVector(), qualitative, *this->linearEquationSolverFactory)));
}
template<class ValueType>
storm::models::sparse::Ctmc<ValueType> const& SparseCtmcCslModelChecker<ValueType>::getModel() const {
return this->template getModelAs<storm::models::sparse::Ctmc<ValueType>>();
}
template<class ValueType>
std::vector<ValueType> SparseCtmcCslModelChecker<ValueType>::computeBoundedUntilProbabilitiesHelper(storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, std::vector<ValueType> const& exitRates, bool qualitative, double lowerBound, double upperBound) const {
// If the time bounds are [0, inf], we rather call untimed reachability.
storm::utility::ConstantsComparator<ValueType> comparator;
if (comparator.isZero(lowerBound) && comparator.isInfinity(upperBound)) {
return this->computeUntilProbabilitiesHelper(this->getModel().getTransitionMatrix(), this->getModel().getBackwardTransitions(), phiStates, psiStates, qualitative, *this->linearEquationSolverFactory);
}
// From this point on, we know that we have to solve a more complicated problem [t, t'] with either t != 0
// or t' != inf.
// Create the result vector.
std::vector<ValueType> result;
// If we identify the states that have probability 0 of reaching the target states, we can exclude them from the
// further computations.
storm::storage::SparseMatrix<ValueType> backwardTransitions = this->getModel().getBackwardTransitions();
storm::storage::BitVector statesWithProbabilityGreater0 = storm::utility::graph::performProbGreater0(backwardTransitions, phiStates, psiStates);
STORM_LOG_INFO("Found " << statesWithProbabilityGreater0.getNumberOfSetBits() << " states with probability greater 0.");
storm::storage::BitVector statesWithProbabilityGreater0NonPsi = statesWithProbabilityGreater0 & ~psiStates;
STORM_LOG_INFO("Found " << statesWithProbabilityGreater0NonPsi.getNumberOfSetBits() << " 'maybe' states.");
if (!statesWithProbabilityGreater0NonPsi.empty()) {
if (comparator.isZero(upperBound)) {
// In this case, the interval is of the form [0, 0].
result = std::vector<ValueType>(this->getModel().getNumberOfStates(), storm::utility::zero<ValueType>());
storm::utility::vector::setVectorValues<ValueType>(result, psiStates, storm::utility::one<ValueType>());
} else {
if (comparator.isZero(lowerBound)) {
// In this case, the interval is of the form [0, t].
// Note that this excludes [0, inf] since this is untimed reachability and we considered this case earlier.
// Find the maximal rate of all 'maybe' states to take it as the uniformization rate.
ValueType uniformizationRate = 0;
for (auto const& state : statesWithProbabilityGreater0NonPsi) {
uniformizationRate = std::max(uniformizationRate, exitRates[state]);
}
uniformizationRate *= 1.02;
STORM_LOG_THROW(uniformizationRate > 0, storm::exceptions::InvalidStateException, "The uniformization rate must be positive.");
// Compute the uniformized matrix.
storm::storage::SparseMatrix<ValueType> uniformizedMatrix = this->computeUniformizedMatrix(this->getModel().getTransitionMatrix(), statesWithProbabilityGreater0NonPsi, uniformizationRate, exitRates);
// Compute the vector that is to be added as a compensation for removing the absorbing states.
std::vector<ValueType> b = this->getModel().getTransitionMatrix().getConstrainedRowSumVector(statesWithProbabilityGreater0NonPsi, psiStates);
for (auto& element : b) {
element /= uniformizationRate;
}
// Finally compute the transient probabilities.
std::vector<ValueType> values(statesWithProbabilityGreater0NonPsi.getNumberOfSetBits(), storm::utility::zero<ValueType>());
std::vector<ValueType> subresult = this->computeTransientProbabilities(uniformizedMatrix, &b, upperBound, uniformizationRate, values, *this->linearEquationSolverFactory);
result = std::vector<ValueType>(this->getModel().getNumberOfStates(), storm::utility::zero<ValueType>());
storm::utility::vector::setVectorValues(result, statesWithProbabilityGreater0NonPsi, subresult);
storm::utility::vector::setVectorValues(result, psiStates, storm::utility::one<ValueType>());
} else if (comparator.isInfinity(upperBound)) {
// In this case, the interval is of the form [t, inf] with t != 0.
// Start by computing the (unbounded) reachability probabilities of reaching psi states while
// staying in phi states.
result = this->computeUntilProbabilitiesHelper(this->getModel().getTransitionMatrix(), backwardTransitions, phiStates, psiStates, qualitative, *this->linearEquationSolverFactory);
// Determine the set of states that must be considered further.
storm::storage::BitVector relevantStates = statesWithProbabilityGreater0 & phiStates;
std::vector<ValueType> subResult(relevantStates.getNumberOfSetBits());
storm::utility::vector::selectVectorValues(subResult, relevantStates, result);
ValueType uniformizationRate = 0;
for (auto const& state : relevantStates) {
uniformizationRate = std::max(uniformizationRate, exitRates[state]);
}
uniformizationRate *= 1.02;
STORM_LOG_THROW(uniformizationRate > 0, storm::exceptions::InvalidStateException, "The uniformization rate must be positive.");
// Compute the uniformized matrix.
storm::storage::SparseMatrix<ValueType> uniformizedMatrix = this->computeUniformizedMatrix(this->getModel().getTransitionMatrix(), relevantStates, uniformizationRate, exitRates);
// Compute the transient probabilities.
subResult = this->computeTransientProbabilities(uniformizedMatrix, nullptr, lowerBound, uniformizationRate, subResult, *this->linearEquationSolverFactory);
// Fill in the correct values.
storm::utility::vector::setVectorValues(result, ~relevantStates, storm::utility::zero<ValueType>());
storm::utility::vector::setVectorValues(result, relevantStates, subResult);
} else {
// In this case, the interval is of the form [t, t'] with t != 0 and t' != inf.
// Prepare some variables that are used by the two following blocks.
ValueType uniformizationRate = 0;
storm::storage::SparseMatrix<ValueType> uniformizedMatrix;
std::vector<ValueType> newSubresult;
if (lowerBound != upperBound) {
// Find the maximal rate of all 'maybe' states to take it as the uniformization rate.
uniformizationRate = 0;
for (auto const& state : statesWithProbabilityGreater0NonPsi) {
uniformizationRate = std::max(uniformizationRate, exitRates[state]);
}
uniformizationRate *= 1.02;
STORM_LOG_THROW(uniformizationRate > 0, storm::exceptions::InvalidStateException, "The uniformization rate must be positive.");
// Compute the (first) uniformized matrix.
uniformizedMatrix = this->computeUniformizedMatrix(this->getModel().getTransitionMatrix(), statesWithProbabilityGreater0NonPsi, uniformizationRate, exitRates);
// Compute the vector that is to be added as a compensation for removing the absorbing states.
std::vector<ValueType> b = this->getModel().getTransitionMatrix().getConstrainedRowSumVector(statesWithProbabilityGreater0NonPsi, psiStates);
for (auto& element : b) {
element /= uniformizationRate;
}
// Start by computing the transient probabilities of reaching a psi state in time t' - t.
std::vector<ValueType> values(statesWithProbabilityGreater0NonPsi.getNumberOfSetBits(), storm::utility::zero<ValueType>());
std::vector<ValueType> subresult = computeTransientProbabilities(uniformizedMatrix, &b, upperBound - lowerBound, uniformizationRate, values, *this->linearEquationSolverFactory);
storm::storage::BitVector relevantStates = statesWithProbabilityGreater0 & phiStates;
newSubresult = std::vector<ValueType>(relevantStates.getNumberOfSetBits());
storm::utility::vector::selectVectorValues(newSubresult, statesWithProbabilityGreater0NonPsi % relevantStates, subresult);
storm::utility::vector::setVectorValues(newSubresult, psiStates % relevantStates, storm::utility::one<ValueType>());
// Then compute the transient probabilities of being in such a state after t time units. For this,
// we must re-uniformize the CTMC, so we need to compute the second uniformized matrix.
uniformizationRate = 0;
for (auto const& state : relevantStates) {
uniformizationRate = std::max(uniformizationRate, exitRates[state]);
}
uniformizationRate *= 1.02;
STORM_LOG_THROW(uniformizationRate > 0, storm::exceptions::InvalidStateException, "The uniformization rate must be positive.");
// Finally, we compute the second set of transient probabilities.
uniformizedMatrix = this->computeUniformizedMatrix(this->getModel().getTransitionMatrix(), relevantStates, uniformizationRate, exitRates);
newSubresult = computeTransientProbabilities(uniformizedMatrix, nullptr, lowerBound, uniformizationRate, newSubresult, *this->linearEquationSolverFactory);
// Fill in the correct values.
result = std::vector<ValueType>(this->getModel().getNumberOfStates(), storm::utility::zero<ValueType>());
storm::utility::vector::setVectorValues(result, ~relevantStates, storm::utility::zero<ValueType>());
storm::utility::vector::setVectorValues(result, relevantStates, newSubresult);
} else {
newSubresult = std::vector<ValueType>(statesWithProbabilityGreater0.getNumberOfSetBits());
storm::utility::vector::setVectorValues(newSubresult, psiStates % statesWithProbabilityGreater0, storm::utility::one<ValueType>());
// Then compute the transient probabilities of being in such a state after t time units. For this,
// we must re-uniformize the CTMC, so we need to compute the second uniformized matrix.
uniformizationRate = 0;
for (auto const& state : statesWithProbabilityGreater0) {
uniformizationRate = std::max(uniformizationRate, exitRates[state]);
}
uniformizationRate *= 1.02;
STORM_LOG_THROW(uniformizationRate > 0, storm::exceptions::InvalidStateException, "The uniformization rate must be positive.");
// Finally, we compute the second set of transient probabilities.
uniformizedMatrix = this->computeUniformizedMatrix(this->getModel().getTransitionMatrix(), statesWithProbabilityGreater0, uniformizationRate, exitRates);
newSubresult = computeTransientProbabilities(uniformizedMatrix, nullptr, lowerBound, uniformizationRate, newSubresult, *this->linearEquationSolverFactory);
// Fill in the correct values.
result = std::vector<ValueType>(this->getModel().getNumberOfStates(), storm::utility::zero<ValueType>());
storm::utility::vector::setVectorValues(result, ~statesWithProbabilityGreater0, storm::utility::zero<ValueType>());
storm::utility::vector::setVectorValues(result, statesWithProbabilityGreater0, newSubresult);
}
}
}
}
return result;
}
template<class ValueType>
storm::storage::SparseMatrix<ValueType> SparseCtmcCslModelChecker<ValueType>::computeUniformizedMatrix(storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::BitVector const& maybeStates, ValueType uniformizationRate, std::vector<ValueType> const& exitRates) {
STORM_LOG_DEBUG("Computing uniformized matrix using uniformization rate " << uniformizationRate << ".");
STORM_LOG_DEBUG("Keeping " << maybeStates.getNumberOfSetBits() << " rows.");
// Create the submatrix that only contains the states with a positive probability (including the
// psi states) and reserve space for elements on the diagonal.
storm::storage::SparseMatrix<ValueType> uniformizedMatrix = transitionMatrix.getSubmatrix(false, maybeStates, maybeStates, true);
// Now we need to perform the actual uniformization. That is, all entries need to be divided by
// the uniformization rate, and the diagonal needs to be set to the negative exit rate of the
// state plus the self-loop rate and then increased by one.
uint_fast64_t currentRow = 0;
for (auto const& state : maybeStates) {
for (auto& element : uniformizedMatrix.getRow(currentRow)) {
if (element.getColumn() == currentRow) {
element.setValue((element.getValue() - exitRates[state]) / uniformizationRate + storm::utility::one<ValueType>());
} else {
element.setValue(element.getValue() / uniformizationRate);
}
}
++currentRow;
}
return uniformizedMatrix;
}
template<class ValueType>
template<bool computeCumulativeReward>
std::vector<ValueType> SparseCtmcCslModelChecker<ValueType>::computeTransientProbabilities(storm::storage::SparseMatrix<ValueType> const& uniformizedMatrix, std::vector<ValueType> const* addVector, ValueType timeBound, ValueType uniformizationRate, std::vector<ValueType> values, storm::utility::solver::LinearEquationSolverFactory<ValueType> const& linearEquationSolverFactory) {
ValueType lambda = timeBound * uniformizationRate;
// If no time can pass, the current values are the result.
if (lambda == storm::utility::zero<ValueType>()) {
return values;
}
// Use Fox-Glynn to get the truncation points and the weights.
std::tuple<uint_fast64_t, uint_fast64_t, ValueType, std::vector<ValueType>> foxGlynnResult = storm::utility::numerical::getFoxGlynnCutoff(lambda, 1e-300, 1e+300, storm::settings::generalSettings().getPrecision() / 8.0);
STORM_LOG_DEBUG("Fox-Glynn cutoff points: left=" << std::get<0>(foxGlynnResult) << ", right=" << std::get<1>(foxGlynnResult));
// Scale the weights so they add up to one.
for (auto& element : std::get<3>(foxGlynnResult)) {
element /= std::get<2>(foxGlynnResult);
}
// If the cumulative reward is to be computed, we need to adjust the weights.
if (computeCumulativeReward) {
ValueType sum = storm::utility::zero<ValueType>();
for (auto& element : std::get<3>(foxGlynnResult)) {
sum += element;
element = (1 - sum) / uniformizationRate;
}
}
STORM_LOG_DEBUG("Starting iterations with " << uniformizedMatrix.getRowCount() << " x " << uniformizedMatrix.getColumnCount() << " matrix.");
// Initialize result.
std::vector<ValueType> result;
uint_fast64_t startingIteration = std::get<0>(foxGlynnResult);
if (startingIteration == 0) {
result = values;
storm::utility::vector::scaleVectorInPlace(result, std::get<3>(foxGlynnResult)[0]);
std::function<ValueType(ValueType const&, ValueType const&)> addAndScale = [&foxGlynnResult] (ValueType const& a, ValueType const& b) { return a + std::get<3>(foxGlynnResult)[0] * b; };
if (addVector != nullptr) {
storm::utility::vector::applyPointwise(result, *addVector, result, addAndScale);
}
++startingIteration;
} else {
if (computeCumulativeReward) {
result = std::vector<ValueType>(values.size());
std::function<ValueType (ValueType const&)> scaleWithUniformizationRate = [&uniformizationRate] (ValueType const& a) -> ValueType { return a / uniformizationRate; };
storm::utility::vector::applyPointwise(result, result, scaleWithUniformizationRate);
} else {
result = std::vector<ValueType>(values.size());
}
}
std::vector<ValueType> multiplicationResult(result.size());
std::unique_ptr<storm::solver::LinearEquationSolver<ValueType>> solver = linearEquationSolverFactory.create(uniformizedMatrix);
if (!computeCumulativeReward && std::get<0>(foxGlynnResult) > 1) {
// Perform the matrix-vector multiplications (without adding).
solver->performMatrixVectorMultiplication(values, addVector, std::get<0>(foxGlynnResult) - 1, &multiplicationResult);
} else if (computeCumulativeReward) {
std::function<ValueType(ValueType const&, ValueType const&)> addAndScale = [&uniformizationRate] (ValueType const& a, ValueType const& b) { return a + b / uniformizationRate; };
// For the iterations below the left truncation point, we need to add and scale the result with the uniformization rate.
for (uint_fast64_t index = 1; index < startingIteration; ++index) {
solver->performMatrixVectorMultiplication(values, nullptr, 1, &multiplicationResult);
storm::utility::vector::applyPointwise(result, values, result, addAndScale);
}
}
// For the indices that fall in between the truncation points, we need to perform the matrix-vector
// multiplication, scale and add the result.
ValueType weight = 0;
std::function<ValueType(ValueType const&, ValueType const&)> addAndScale = [&weight] (ValueType const& a, ValueType const& b) { return a + weight * b; };
for (uint_fast64_t index = startingIteration; index <= std::get<1>(foxGlynnResult); ++index) {
solver->performMatrixVectorMultiplication(values, addVector, 1, &multiplicationResult);
weight = std::get<3>(foxGlynnResult)[index - std::get<0>(foxGlynnResult)];
storm::utility::vector::applyPointwise(result, values, result, addAndScale);
}
return result;
}
template<class ValueType>
std::vector<ValueType> SparseCtmcCslModelChecker<ValueType>::computeUntilProbabilitiesHelper(storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, bool qualitative, storm::utility::solver::LinearEquationSolverFactory<ValueType> const& linearEquationSolverFactory) {
return SparseDtmcPrctlModelChecker<ValueType>::computeUntilProbabilitiesHelper(transitionMatrix, backwardTransitions, phiStates, psiStates, qualitative, linearEquationSolverFactory);
}
template<class ValueType>
std::unique_ptr<CheckResult> SparseCtmcCslModelChecker<ValueType>::computeInstantaneousRewards(storm::logic::InstantaneousRewardFormula const& rewardPathFormula, bool qualitative, boost::optional<storm::logic::OptimalityType> const& optimalityType) {
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(this->computeInstantaneousRewardsHelper(rewardPathFormula.getContinuousTimeBound())));
}
template<class ValueType>
std::vector<ValueType> SparseCtmcCslModelChecker<ValueType>::computeInstantaneousRewardsHelper(double timeBound) const {
// Only compute the result if the model has a state-based reward this->getModel().
STORM_LOG_THROW(this->getModel().hasStateRewards(), storm::exceptions::InvalidPropertyException, "Missing reward model for formula. Skipping formula.");
// Initialize result to state rewards of the this->getModel().
std::vector<ValueType> result(this->getModel().getStateRewardVector());
// If the time-bound is not zero, we need to perform a transient analysis.
if (timeBound > 0) {
ValueType uniformizationRate = 0;
for (auto const& rate : this->getModel().getExitRateVector()) {
uniformizationRate = std::max(uniformizationRate, rate);
}
uniformizationRate *= 1.02;
STORM_LOG_THROW(uniformizationRate > 0, storm::exceptions::InvalidStateException, "The uniformization rate must be positive.");
storm::storage::SparseMatrix<ValueType> uniformizedMatrix = this->computeUniformizedMatrix(this->getModel().getTransitionMatrix(), storm::storage::BitVector(this->getModel().getNumberOfStates(), true), uniformizationRate, this->getModel().getExitRateVector());
result = this->computeTransientProbabilities(uniformizedMatrix, nullptr, timeBound, uniformizationRate, result, *this->linearEquationSolverFactory);
}
return result;
}
template<class ValueType>
std::unique_ptr<CheckResult> SparseCtmcCslModelChecker<ValueType>::computeCumulativeRewards(storm::logic::CumulativeRewardFormula const& rewardPathFormula, bool qualitative, boost::optional<storm::logic::OptimalityType> const& optimalityType) {
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(this->computeCumulativeRewardsHelper(rewardPathFormula.getContinuousTimeBound())));
}
template<class ValueType>
std::vector<ValueType> SparseCtmcCslModelChecker<ValueType>::computeCumulativeRewardsHelper(double timeBound) const {
// Only compute the result if the model has a state-based reward this->getModel().
STORM_LOG_THROW(this->getModel().hasStateRewards() || this->getModel().hasTransitionRewards(), storm::exceptions::InvalidPropertyException, "Missing reward model for formula. Skipping formula.");
// If the time bound is zero, the result is the constant zero vector.
if (timeBound == 0) {
return std::vector<ValueType>(this->getModel().getNumberOfStates(), storm::utility::zero<ValueType>());
}
// Otherwise, we need to perform some computations.
// Start with the uniformization.
ValueType uniformizationRate = 0;
for (auto const& rate : this->getModel().getExitRateVector()) {
uniformizationRate = std::max(uniformizationRate, rate);
}
uniformizationRate *= 1.02;
STORM_LOG_THROW(uniformizationRate > 0, storm::exceptions::InvalidStateException, "The uniformization rate must be positive.");
storm::storage::SparseMatrix<ValueType> uniformizedMatrix = this->computeUniformizedMatrix(this->getModel().getTransitionMatrix(), storm::storage::BitVector(this->getModel().getNumberOfStates(), true), uniformizationRate, this->getModel().getExitRateVector());
// Compute the total state reward vector.
std::vector<ValueType> totalRewardVector;
if (this->getModel().hasTransitionRewards()) {
totalRewardVector = this->getModel().getTransitionMatrix().getPointwiseProductRowSumVector(this->getModel().getTransitionRewardMatrix());
if (this->getModel().hasStateRewards()) {
storm::utility::vector::addVectors(totalRewardVector, this->getModel().getStateRewardVector(), totalRewardVector);
}
} else {
totalRewardVector = std::vector<ValueType>(this->getModel().getStateRewardVector());
}
// Finally, compute the transient probabilities.
return this->computeTransientProbabilities<true>(uniformizedMatrix, nullptr, timeBound, uniformizationRate, totalRewardVector, *this->linearEquationSolverFactory);
}
template<class ValueType>
storm::storage::SparseMatrix<ValueType> SparseCtmcCslModelChecker<ValueType>::computeProbabilityMatrix(storm::storage::SparseMatrix<ValueType> const& rateMatrix, std::vector<ValueType> const& exitRates) {
// Turn the rates into probabilities by scaling each row with the exit rate of the state.
storm::storage::SparseMatrix<ValueType> result(rateMatrix);
for (uint_fast64_t row = 0; row < result.getRowCount(); ++row) {
for (auto& entry : result.getRow(row)) {
entry.setValue(entry.getValue() / exitRates[row]);
}
}
return result;
}
template<class ValueType>
std::unique_ptr<CheckResult> SparseCtmcCslModelChecker<ValueType>::computeReachabilityRewards(storm::logic::ReachabilityRewardFormula const& rewardPathFormula, bool qualitative, boost::optional<storm::logic::OptimalityType> const& optimalityType) {
std::unique_ptr<CheckResult> subResultPointer = this->check(rewardPathFormula.getSubformula());
ExplicitQualitativeCheckResult const& subResult = subResultPointer->asExplicitQualitativeCheckResult();
storm::storage::SparseMatrix<ValueType> probabilityMatrix = computeProbabilityMatrix(this->getModel().getTransitionMatrix(), this->getModel().getExitRateVector());
boost::optional<std::vector<ValueType>> modifiedStateRewardVector;
if (this->getModel().hasStateRewards()) {
modifiedStateRewardVector = std::vector<ValueType>(this->getModel().getStateRewardVector());
typename std::vector<ValueType>::const_iterator it2 = this->getModel().getExitRateVector().begin();
for (typename std::vector<ValueType>::iterator it1 = modifiedStateRewardVector.get().begin(), ite1 = modifiedStateRewardVector.get().end(); it1 != ite1; ++it1, ++it2) {
*it1 /= *it2;
}
}
return std::unique_ptr<CheckResult>(new ExplicitQuantitativeCheckResult<ValueType>(SparseDtmcPrctlModelChecker<ValueType>::computeReachabilityRewardsHelper(probabilityMatrix, modifiedStateRewardVector, this->getModel().getOptionalTransitionRewardMatrix(), this->getModel().getBackwardTransitions(), subResult.getTruthValuesVector(), *linearEquationSolverFactory, qualitative)));
}
// Explicitly instantiate the model checker.
template class SparseCtmcCslModelChecker<double>;
} // namespace modelchecker
} // namespace storm