530 lines
30 KiB
530 lines
30 KiB
/*
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* SparseDtmcPrctlModelChecker.h
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*
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* Created on: 22.10.2012
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* Author: Thomas Heinemann
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*/
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#ifndef STORM_MODELCHECKER_PRCTL_SPARSEDTMCPRCTLMODELCHECKER_H_
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#define STORM_MODELCHECKER_PRCTL_SPARSEDTMCPRCTLMODELCHECKER_H_
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#include "src/modelchecker/prctl/AbstractModelChecker.h"
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#include "src/models/Dtmc.h"
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#include "src/solver/LinearEquationSolver.h"
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#include "src/utility/vector.h"
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#include "src/utility/graph.h"
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#include <vector>
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namespace storm {
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namespace modelchecker {
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namespace prctl {
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/*!
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* @brief
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* Interface for all model checkers that can verify PRCTL formulae over DTMCs represented as a sparse matrix.
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*/
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template<class Type>
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class SparseDtmcPrctlModelChecker : public AbstractModelChecker<Type> {
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public:
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/*!
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* Constructs a SparseDtmcPrctlModelChecker with the given model.
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*
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* @param model The DTMC to be checked.
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*/
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explicit SparseDtmcPrctlModelChecker(storm::models::Dtmc<Type> const& model, storm::solver::LinearEquationSolver<Type>* linearEquationSolver) : AbstractModelChecker<Type>(model), linearEquationSolver(linearEquationSolver) {
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// Intentionally left empty.
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}
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/*!
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* Copy constructs a SparseDtmcPrctlModelChecker from the given model checker. In particular, this means that the newly
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* constructed model checker will have the model of the given model checker as its associated model.
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*/
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explicit SparseDtmcPrctlModelChecker(storm::modelchecker::prctl::SparseDtmcPrctlModelChecker<Type> const& modelChecker) : AbstractModelChecker<Type>(modelChecker), linearEquationSolver(modelChecker.linearEquationSolver->clone()) {
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// Intentionally left empty.
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}
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/*!
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* Virtual destructor. Needs to be virtual, because this class has virtual methods.
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*/
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virtual ~SparseDtmcPrctlModelChecker() {
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// Intentionally left empty.
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}
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/*!
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* Returns a constant reference to the DTMC associated with this model checker.
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* @returns A constant reference to the DTMC associated with this model checker.
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*/
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storm::models::Dtmc<Type> const& getModel() const {
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return AbstractModelChecker<Type>::template getModel<storm::models::Dtmc<Type>>();
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}
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/*!
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* Checks the given formula that is a bounded-until formula.
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*
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* @param formula The formula to check.
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* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
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* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
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* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
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* bounds 0 and 1.
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* @returns The probabilities for the given formula to hold on every state of the model associated with this model
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* checker. If the qualitative flag is set, exact probabilities might not be computed.
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*/
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virtual std::vector<Type> checkBoundedUntil(storm::property::prctl::BoundedUntil<Type> const& formula, bool qualitative) const {
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return this->checkBoundedUntil(formula.getLeft().check(*this), formula.getRight().check(*this), formula.getBound(), qualitative);
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}
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/*!
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* Computes the probability to satisfy phi until psi inside a given bound for each state in the model.
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*
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* @param phiStates A bit vector indicating which states satisfy phi.
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* @param psiStates A bit vector indicating which states satisfy psi.
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* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
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* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
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* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
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* bounds 0 and 1.
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* @returns The probabilities for the given formula to hold on every state of the model associated with this model
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* checker. If the qualitative flag is set, exact probabilities might not be computed.
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*/
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virtual std::vector<Type> checkBoundedUntil(storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, uint_fast64_t stepBound, bool qualitative) const {
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std::vector<Type> result(this->getModel().getNumberOfStates());
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// If we identify the states that have probability 0 of reaching the target states, we can exclude them in the
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// further analysis.
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storm::storage::BitVector statesWithProbabilityGreater0 = storm::utility::graph::performProbGreater0(this->getModel(), this->getModel().getBackwardTransitions(), phiStates, psiStates, true, stepBound);
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LOG4CPLUS_INFO(logger, "Found " << statesWithProbabilityGreater0.getNumberOfSetBits() << " 'maybe' states.");
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// Check if we already know the result (i.e. probability 0) for all initial states and
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// don't compute anything in this case.
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if (this->getModel().getInitialStates().isDisjointFrom(statesWithProbabilityGreater0)) {
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LOG4CPLUS_INFO(logger, "The probabilities for the initial states were determined in a preprocessing step."
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<< " No exact probabilities were computed.");
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// Set the values for all maybe-states to 0.5 to indicate that their probability values are not 0 (and
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// not necessarily 1).
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storm::utility::vector::setVectorValues(result, statesWithProbabilityGreater0, Type(0.5));
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} else {
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// In this case we have have to compute the probabilities.
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// We can eliminate the rows and columns from the original transition probability matrix that have probability 0.
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storm::storage::SparseMatrix<Type> submatrix = this->getModel().getTransitionMatrix().getSubmatrix(true, statesWithProbabilityGreater0, statesWithProbabilityGreater0, true);
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// Compute the new set of target states in the reduced system.
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storm::storage::BitVector rightStatesInReducedSystem = psiStates % statesWithProbabilityGreater0;
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// Make all rows absorbing that satisfy the second sub-formula.
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submatrix.makeRowsAbsorbing(rightStatesInReducedSystem);
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// Create the vector with which to multiply.
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std::vector<Type> subresult(statesWithProbabilityGreater0.getNumberOfSetBits());
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storm::utility::vector::setVectorValues(subresult, rightStatesInReducedSystem, storm::utility::constantOne<Type>());
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// Perform the matrix vector multiplication as often as required by the formula bound.
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if (linearEquationSolver != nullptr) {
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this->linearEquationSolver->performMatrixVectorMultiplication(submatrix, subresult, nullptr, stepBound);
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} else {
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throw storm::exceptions::InvalidStateException() << "No valid linear equation solver available.";
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}
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// Set the values of the resulting vector accordingly.
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storm::utility::vector::setVectorValues(result, statesWithProbabilityGreater0, subresult);
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storm::utility::vector::setVectorValues<Type>(result, ~statesWithProbabilityGreater0, storm::utility::constantZero<Type>());
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}
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return result;
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}
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/*!
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* Checks the given formula that is a next formula.
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*
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* @param formula The formula to check.
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* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
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* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
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* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
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* bounds 0 and 1.
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* @returns The probabilities for the given formula to hold on every state of the model associated with this model
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* checker. If the qualitative flag is set, exact probabilities might not be computed.
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*/
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virtual std::vector<Type> checkNext(storm::property::prctl::Next<Type> const& formula, bool qualitative) const {
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// First, we need to compute the states that satisfy the child formula of the next-formula.
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storm::storage::BitVector nextStates = formula.getChild().check(*this);
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// Create the vector with which to multiply and initialize it correctly.
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std::vector<Type> result(this->getModel().getNumberOfStates());
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storm::utility::vector::setVectorValues(result, nextStates, storm::utility::constantOne<Type>());
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// Perform one single matrix-vector multiplication.
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if (linearEquationSolver != nullptr) {
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this->linearEquationSolver->performMatrixVectorMultiplication(this->getModel().getTransitionMatrix(), result);
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} else {
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throw storm::exceptions::InvalidStateException() << "No valid linear equation solver available.";
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}
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return result;
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}
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/*!
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* Checks the given formula that is a bounded-eventually formula.
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*
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* @param formula The formula to check.
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* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
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* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
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* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
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* bounds 0 and 1.
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* @returns The probabilities for the given formula to hold on every state of the model associated with this model
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* checker. If the qualitative flag is set, exact probabilities might not be computed.
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*/
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virtual std::vector<Type> checkBoundedEventually(storm::property::prctl::BoundedEventually<Type> const& formula, bool qualitative) const {
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return this->checkBoundedUntil(storm::storage::BitVector(this->getModel().getNumberOfStates(), true), formula.getChild().check(*this), formula.getBound(), qualitative);
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}
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/*!
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* Checks the given formula that is an eventually formula.
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*
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* @param formula The formula to check.
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* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
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* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
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* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
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* bounds 0 and 1.
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* @returns The probabilities for the given formula to hold on every state of the model associated with this model
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* checker. If the qualitative flag is set, exact probabilities might not be computed.
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*/
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virtual std::vector<Type> checkEventually(storm::property::prctl::Eventually<Type> const& formula, bool qualitative) const {
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// Create equivalent temporary until formula and check it.
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storm::property::prctl::Until<Type> temporaryUntilFormula(new storm::property::prctl::Ap<Type>("true"), formula.getChild().clone());
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return this->checkUntil(temporaryUntilFormula, qualitative);
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}
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/*!
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* Checks the given formula that is a globally formula.
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*
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* @param formula The formula to check.
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* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
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* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
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* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
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* bounds 0 and 1.
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* @returns The probabilities for the given formula to hold on every state of the model associated with this model
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* checker. If the qualitative flag is set, exact probabilities might not be computed.
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*/
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virtual std::vector<Type> checkGlobally(storm::property::prctl::Globally<Type> const& formula, bool qualitative) const {
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// Create "equivalent" (equivalent up to negation) temporary eventually formula and check it.
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storm::property::prctl::Eventually<Type> temporaryEventuallyFormula(new storm::property::prctl::Not<Type>(formula.getChild().clone()));
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std::vector<Type> result = this->checkEventually(temporaryEventuallyFormula, qualitative);
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// Now subtract the resulting vector from the constant one vector to obtain final result.
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storm::utility::vector::subtractFromConstantOneVector(result);
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return result;
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}
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/*!
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* Check the given formula that is an until formula.
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*
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* @param formula The formula to check.
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* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
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* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
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* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
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* bounds 0 and 1.
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* @returns The probabilities for the given formula to hold on every state of the model associated with this model
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* checker. If the qualitative flag is set, exact probabilities might not be computed.
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*/
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virtual std::vector<Type> checkUntil(storm::property::prctl::Until<Type> const& formula, bool qualitative) const {
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return this->checkUntil(formula.getLeft().check(*this), formula.getRight().check(*this), qualitative);
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}
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/*!
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* Computes the probability to satisfy phi until psi for each state in the model.
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*
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* @param phiStates A bit vector indicating which states satisfy phi.
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* @param psiStates A bit vector indicating which states satisfy psi.
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* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
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* results are only compared against the bounds 0 and 1. If set to true, this will most likely results that are only
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* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
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* bounds 0 and 1.
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* @returns The probabilities for the given formula to hold on every state of the model associated with this model
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* checker. If the qualitative flag is set, exact probabilities might not be computed.
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*/
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virtual std::vector<Type> checkUntil(storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, bool qualitative) const {
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// We need to identify the states which have to be taken out of the matrix, i.e.
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// all states that have probability 0 and 1 of satisfying the until-formula.
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std::pair<storm::storage::BitVector, storm::storage::BitVector> statesWithProbability01 = storm::utility::graph::performProb01(this->getModel(), phiStates, psiStates);
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storm::storage::BitVector statesWithProbability0 = std::move(statesWithProbability01.first);
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storm::storage::BitVector statesWithProbability1 = std::move(statesWithProbability01.second);
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// Perform some logging.
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storm::storage::BitVector maybeStates = ~(statesWithProbability0 | statesWithProbability1);
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LOG4CPLUS_INFO(logger, "Found " << statesWithProbability0.getNumberOfSetBits() << " 'no' states.");
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LOG4CPLUS_INFO(logger, "Found " << statesWithProbability1.getNumberOfSetBits() << " 'yes' states.");
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LOG4CPLUS_INFO(logger, "Found " << maybeStates.getNumberOfSetBits() << " 'maybe' states.");
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// Create resulting vector.
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std::vector<Type> result(this->getModel().getNumberOfStates());
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// Check whether we need to compute exact probabilities for some states.
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if (this->getModel().getInitialStates().isDisjointFrom(maybeStates) || qualitative) {
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if (qualitative) {
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LOG4CPLUS_INFO(logger, "The formula was checked qualitatively. No exact probabilities were computed.");
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} else {
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LOG4CPLUS_INFO(logger, "The probabilities for the initial states were determined in a preprocessing step."
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<< " No exact probabilities were computed.");
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}
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// Set the values for all maybe-states to 0.5 to indicate that their probability values
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// are neither 0 nor 1.
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storm::utility::vector::setVectorValues<Type>(result, maybeStates, Type(0.5));
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} else {
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// In this case we have have to compute the probabilities.
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// We can eliminate the rows and columns from the original transition probability matrix.
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storm::storage::SparseMatrix<Type> submatrix = this->getModel().getTransitionMatrix().getSubmatrix(true, maybeStates, maybeStates, true);
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// Converting the matrix from the fixpoint notation to the form needed for the equation
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// system. That is, we go from x = A*x + b to (I-A)x = b.
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submatrix.convertToEquationSystem();
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// Initialize the x vector with 0.5 for each element. This is the initial guess for
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// the iterative solvers. It should be safe as for all 'maybe' states we know that the
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// probability is strictly larger than 0.
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std::vector<Type> x(maybeStates.getNumberOfSetBits(), Type(0.5));
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// Prepare the right-hand side of the equation system. For entry i this corresponds to
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// the accumulated probability of going from state i to some 'yes' state.
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std::vector<Type> b = this->getModel().getTransitionMatrix().getConstrainedRowSumVector(maybeStates, statesWithProbability1);
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// Now solve the created system of linear equations.
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if (linearEquationSolver != nullptr) {
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this->linearEquationSolver->solveEquationSystem(submatrix, x, b);
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} else {
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throw storm::exceptions::InvalidStateException() << "No valid linear equation solver available.";
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}
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// Set values of resulting vector according to result.
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storm::utility::vector::setVectorValues<Type>(result, maybeStates, x);
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}
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// Set values of resulting vector that are known exactly.
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storm::utility::vector::setVectorValues<Type>(result, statesWithProbability0, storm::utility::constantZero<Type>());
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storm::utility::vector::setVectorValues<Type>(result, statesWithProbability1, storm::utility::constantOne<Type>());
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return result;
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}
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/*!
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* Checks the given formula that is an instantaneous reward formula.
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*
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* @param formula The formula to check.
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* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
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* results are only compared against the bound 0. If set to true, this will most likely results that are only
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* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
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* bound 0.
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* @returns The reward values for the given formula for every state of the model associated with this model
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* checker. If the qualitative flag is set, exact values might not be computed.
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*/
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virtual std::vector<Type> checkInstantaneousReward(storm::property::prctl::InstantaneousReward<Type> const& formula, bool qualitative) const {
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// Only compute the result if the model has a state-based reward model.
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if (!this->getModel().hasStateRewards()) {
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LOG4CPLUS_ERROR(logger, "Missing (state-based) reward model for formula.");
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throw storm::exceptions::InvalidPropertyException() << "Missing (state-based) reward model for formula.";
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}
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// Initialize result to state rewards of the model.
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std::vector<Type> result(this->getModel().getStateRewardVector());
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// Perform the actual matrix-vector multiplication as long as the bound of the formula is met.
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if (linearEquationSolver != nullptr) {
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this->linearEquationSolver->performMatrixVectorMultiplication(this->getModel().getTransitionMatrix(), result, nullptr, formula.getBound());
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} else {
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throw storm::exceptions::InvalidStateException() << "No valid linear equation solver available.";
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}
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return result;
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}
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/*!
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* Check the given formula that is a cumulative reward formula.
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*
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* @param formula The formula to check.
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* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
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* results are only compared against the bound 0. If set to true, this will most likely results that are only
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* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
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* bound 0.
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* @returns The reward values for the given formula for every state of the model associated with this model
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* checker. If the qualitative flag is set, exact values might not be computed.
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*/
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virtual std::vector<Type> checkCumulativeReward(storm::property::prctl::CumulativeReward<Type> const& formula, bool qualitative) const {
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// Only compute the result if the model has at least one reward model.
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if (!this->getModel().hasStateRewards() && !this->getModel().hasTransitionRewards()) {
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LOG4CPLUS_ERROR(logger, "Missing reward model for formula.");
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throw storm::exceptions::InvalidPropertyException() << "Missing reward model for formula.";
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}
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// Compute the reward vector to add in each step based on the available reward models.
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std::vector<Type> totalRewardVector;
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if (this->getModel().hasTransitionRewards()) {
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totalRewardVector = this->getModel().getTransitionMatrix().getPointwiseProductRowSumVector(this->getModel().getTransitionRewardMatrix());
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if (this->getModel().hasStateRewards()) {
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storm::utility::vector::addVectorsInPlace(totalRewardVector, this->getModel().getStateRewardVector());
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}
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} else {
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totalRewardVector = std::vector<Type>(this->getModel().getStateRewardVector());
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}
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// Initialize result to either the state rewards of the model or the null vector.
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std::vector<Type> result;
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if (this->getModel().hasStateRewards()) {
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result = std::vector<Type>(this->getModel().getStateRewardVector());
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} else {
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result.resize(this->getModel().getNumberOfStates());
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}
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// Perform the actual matrix-vector multiplication as long as the bound of the formula is met.
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if (linearEquationSolver != nullptr) {
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this->linearEquationSolver->performMatrixVectorMultiplication(this->getModel().getTransitionMatrix(), result, &totalRewardVector, formula.getBound());
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} else {
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throw storm::exceptions::InvalidStateException() << "No valid linear equation solver available.";
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}
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return result;
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}
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/*!
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* Checks the given formula that is a reachability reward formula.
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*
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* @param formula The formula to check.
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* @param qualitative A flag indicating whether the formula only needs to be evaluated qualitatively, i.e. if the
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* results are only compared against the bound 0. If set to true, this will most likely results that are only
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* qualitatively correct, i.e. do not represent the correct value, but only the correct relation with respect to the
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* bound 0.
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* @returns The reward values for the given formula for every state of the model associated with this model
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* checker. If the qualitative flag is set, exact values might not be computed.
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*/
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virtual std::vector<Type> checkReachabilityReward(storm::property::prctl::ReachabilityReward<Type> const& formula, bool qualitative) const {
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// Only compute the result if the model has at least one reward model.
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if (!this->getModel().hasStateRewards() && !this->getModel().hasTransitionRewards()) {
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LOG4CPLUS_ERROR(logger, "Missing reward model for formula. Skipping formula");
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throw storm::exceptions::InvalidPropertyException() << "Missing reward model for formula.";
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}
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// Determine the states for which the target predicate holds.
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storm::storage::BitVector targetStates = formula.getChild().check(*this);
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// Determine which states have a reward of infinity by definition.
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storm::storage::BitVector trueStates(this->getModel().getNumberOfStates(), true);
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storm::storage::BitVector infinityStates = storm::utility::graph::performProb1(this->getModel(), this->getModel().getBackwardTransitions(), trueStates, targetStates);
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infinityStates.complement();
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storm::storage::BitVector maybeStates = ~targetStates & ~infinityStates;
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LOG4CPLUS_INFO(logger, "Found " << infinityStates.getNumberOfSetBits() << " 'infinity' states.");
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LOG4CPLUS_INFO(logger, "Found " << targetStates.getNumberOfSetBits() << " 'target' states.");
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LOG4CPLUS_INFO(logger, "Found " << maybeStates.getNumberOfSetBits() << " 'maybe' states.");
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// Create resulting vector.
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std::vector<Type> result(this->getModel().getNumberOfStates());
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// Check whether we need to compute exact rewards for some states.
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if (this->getModel().getInitialStates().isDisjointFrom(maybeStates)) {
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LOG4CPLUS_INFO(logger, "The rewards for the initial states were determined in a preprocessing step."
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<< " No exact rewards were computed.");
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// Set the values for all maybe-states to 1 to indicate that their reward values
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// are neither 0 nor infinity.
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storm::utility::vector::setVectorValues<Type>(result, maybeStates, storm::utility::constantOne<Type>());
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} else {
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// In this case we have to compute the reward values for the remaining states.
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// We can eliminate the rows and columns from the original transition probability matrix.
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storm::storage::SparseMatrix<Type> submatrix = this->getModel().getTransitionMatrix().getSubmatrix(true, maybeStates, maybeStates, true);
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// Converting the matrix from the fixpoint notation to the form needed for the equation
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// system. That is, we go from x = A*x + b to (I-A)x = b.
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submatrix.convertToEquationSystem();
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// Initialize the x vector with 1 for each element. This is the initial guess for
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// the iterative solvers.
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std::vector<Type> x(submatrix.getColumnCount(), storm::utility::constantOne<Type>());
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// Prepare the right-hand side of the equation system.
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std::vector<Type> b(submatrix.getRowCount());
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if (this->getModel().hasTransitionRewards()) {
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// If a transition-based reward model is available, we initialize the right-hand
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// side to the vector resulting from summing the rows of the pointwise product
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// of the transition probability matrix and the transition reward matrix.
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std::vector<Type> pointwiseProductRowSumVector = this->getModel().getTransitionMatrix().getPointwiseProductRowSumVector(this->getModel().getTransitionRewardMatrix());
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storm::utility::vector::selectVectorValues(b, maybeStates, pointwiseProductRowSumVector);
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if (this->getModel().hasStateRewards()) {
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// If a state-based reward model is also available, we need to add this vector
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// as well. As the state reward vector contains entries not just for the states
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// that we still consider (i.e. maybeStates), we need to extract these values
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// first.
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std::vector<Type> subStateRewards(b.size());
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storm::utility::vector::selectVectorValues(subStateRewards, maybeStates, this->getModel().getStateRewardVector());
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storm::utility::vector::addVectorsInPlace(b, subStateRewards);
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}
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} else {
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// If only a state-based reward model is available, we take this vector as the
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// right-hand side. As the state reward vector contains entries not just for the
|
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// states that we still consider (i.e. maybeStates), we need to extract these values
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// first.
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storm::utility::vector::selectVectorValues(b, maybeStates, this->getModel().getStateRewardVector());
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}
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// Now solve the resulting equation system.
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if (linearEquationSolver != nullptr) {
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this->linearEquationSolver->solveEquationSystem(submatrix, x, b);
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} else {
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throw storm::exceptions::InvalidStateException() << "No valid linear equation solver available.";
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}
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// Set values of resulting vector according to result.
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|
storm::utility::vector::setVectorValues<Type>(result, maybeStates, x);
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|
}
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// Set values of resulting vector that are known exactly.
|
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storm::utility::vector::setVectorValues(result, targetStates, storm::utility::constantZero<Type>());
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|
storm::utility::vector::setVectorValues(result, infinityStates, storm::utility::constantInfinity<Type>());
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return result;
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|
}
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|
/*!
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|
* Checks the given formula.
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|
* @note This methods overrides the method of the base class to give an additional warning that declaring that minimal or maximal probabilities
|
|
* should be computed for the formula makes no sense in the context of a deterministic model.
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|
*
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|
* @param formula The formula to check.
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|
* @param minimumOperator True iff minimum probabilities/rewards are to be computed.
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|
* @returns The probabilities to satisfy the formula or the rewards accumulated by it, represented by a vector.
|
|
*/
|
|
virtual std::vector<Type> checkMinMaxOperator(storm::property::prctl::AbstractPathFormula<Type> const & formula, bool minimumOperator) const override {
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|
|
|
LOG4CPLUS_WARN(logger, "Formula contains min/max operator, which is not meaningful over deterministic models.");
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|
|
|
std::vector<Type> result = formula.check(*this, false);
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|
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|
return result;
|
|
}
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|
|
|
/*!
|
|
* Checks the given formula and determines whether minimum or maximum probabilities or rewards are to be computed for the formula.
|
|
* @note This methods overrides the method of the base class to give an additional warning that declaring that minimal or maximal probabilities
|
|
* should be computed for the formula makes no sense in the context of a deterministic model.
|
|
*
|
|
* @param formula The formula to check.
|
|
* @param minimumOperator True iff minimum probabilities/rewards are to be computed.
|
|
* @returns The set of states satisfying the formula represented by a bit vector.
|
|
*/
|
|
virtual storm::storage::BitVector checkMinMaxOperator(storm::property::prctl::AbstractStateFormula<Type> const & formula, bool minimumOperator) const override {
|
|
|
|
LOG4CPLUS_WARN(logger, "Formula contains min/max operator, which is not meaningful over deterministic models.");
|
|
|
|
storm::storage::BitVector result = formula.check(*this);
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|
|
|
return result;
|
|
}
|
|
|
|
private:
|
|
// An object that is used for solving linear equations and performing matrix-vector multiplication.
|
|
std::unique_ptr<storm::solver::LinearEquationSolver<Type>> linearEquationSolver;
|
|
};
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|
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|
} // namespace prctl
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} // namespace modelchecker
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} // namespace storm
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#endif /* STORM_MODELCHECKER_PRCTL_SPARSEDTMCPRCTLMODELCHECKER_H_ */
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