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/*
* DtmcPrctlModelChecker.h
*
* Created on: 22.10.2012
* Author: Thomas Heinemann
*/
#ifndef STORM_MODELCHECKER_DTMCPRCTLMODELCHECKER_H_
#define STORM_MODELCHECKER_DTMCPRCTLMODELCHECKER_H_
#include <vector>
#include "src/formula/Formulas.h"
#include "src/utility/Vector.h"
#include "src/storage/SparseMatrix.h"
#include "src/models/Dtmc.h"
#include "src/storage/BitVector.h"
#include "src/exceptions/InvalidPropertyException.h"
#include "src/utility/Vector.h"
#include "src/utility/GraphAnalyzer.h"
#include "src/modelchecker/AbstractModelChecker.h"
#include "log4cplus/logger.h"
#include "log4cplus/loggingmacros.h"
extern log4cplus::Logger logger;
namespace storm {
namespace modelChecker {
/*!
* @brief
* Interface for model checker classes.
*
* This class provides basic functions that are the same for all subclasses, but mainly only declares
* abstract methods that are to be implemented in concrete instances.
*
* @attention This class is abstract.
*/
template<class Type>
class DtmcPrctlModelChecker : public AbstractModelChecker<Type> {
public:
/*!
* Constructor
*
* @param model The dtmc model which is checked.
*/
explicit DtmcPrctlModelChecker(storm::models::Dtmc<Type>& model) : AbstractModelChecker<Type>(model) {
// Intentionally left empty.
}
/*!
* Copy constructor
*
* @param modelChecker The model checker that is copied.
*/
explicit DtmcPrctlModelChecker(const storm::modelChecker::DtmcPrctlModelChecker<Type>* modelChecker) : AbstractModelChecker<Type>(modelChecker) {
// Intentionally left empty.
}
/*!
* Destructor
*/
virtual ~DtmcPrctlModelChecker() {
// Intentionally left empty.
}
/*!
* @returns A reference to the dtmc of the model checker.
*/
storm::models::Dtmc<Type>& getModel() const {
return AbstractModelChecker<Type>::template getModel<storm::models::Dtmc<Type>>();
}
/*!
* The check method for a state formula with a probabilistic operator node without bounds as root
* in its formula tree
*
* @param formula The state formula to check
* @returns The set of states satisfying the formula, represented by a bit vector
*/
std::vector<Type>* checkNoBoundOperator(const storm::formula::NoBoundOperator<Type>& formula) const {
// Check if the operator was an optimality operator and report a warning in that case.
if (formula.isOptimalityOperator()) {
LOG4CPLUS_WARN(logger, "Formula contains min/max operator which is not meaningful over deterministic models.");
}
return formula.getPathFormula().check(*this, false);
}
/*!
* The check method for a path formula with a Bounded Until operator node as root in its formula tree
*
* @param formula The Bounded Until path formula to check
* @returns for each state the probability that the path formula holds.
*/
virtual std::vector<Type>* checkBoundedUntil(const storm::formula::BoundedUntil<Type>& formula, bool qualitative) const {
// First, we need to compute the states that satisfy the sub-formulas of the until-formula.
storm::storage::BitVector* leftStates = formula.getLeft().check(*this);
storm::storage::BitVector* rightStates = formula.getRight().check(*this);
// Copy the matrix before we make any changes.
storm::storage::SparseMatrix<Type> tmpMatrix(*this->getModel().getTransitionMatrix());
// Make all rows absorbing that violate both sub-formulas or satisfy the second sub-formula.
tmpMatrix.makeRowsAbsorbing(~(*leftStates | *rightStates) | *rightStates);
// Delete obsolete intermediates.
delete leftStates;
delete rightStates;
// Create the vector with which to multiply.
std::vector<Type>* result = new std::vector<Type>(this->getModel().getNumberOfStates());
storm::utility::setVectorValues(result, *rightStates, storm::utility::constGetOne<Type>());
// Perform the matrix vector multiplication as often as required by the formula bound.
this->performMatrixVectorMultiplication(tmpMatrix, *result, nullptr, formula.getBound());
// Return result.
return result;
}
/*!
* The check method for a path formula with a Next operator node as root in its formula tree
*
* @param formula The Next path formula to check
* @returns for each state the probability that the path formula holds.
*/
virtual std::vector<Type>* checkNext(const storm::formula::Next<Type>& formula, bool qualitative) const {
// First, we need to compute the states that satisfy the child formula of the next-formula.
storm::storage::BitVector* nextStates = formula.getChild().check(*this);
// Create the vector with which to multiply and initialize it correctly.
std::vector<Type>* result = new std::vector<Type>(this->getModel().getNumberOfStates());
storm::utility::setVectorValues(result, *nextStates, storm::utility::constGetOne<Type>());
// Delete obsolete intermediate.
delete nextStates;
// Perform one single matrix-vector multiplication.
this->performMatrixVectorMultiplication(*this->getModel().getTransitionMatrix(), *result);
// Return result.
return result;
}
/*!
* The check method for a path formula with a Bounded Eventually operator node as root in its
* formula tree
*
* @param formula The Bounded Eventually path formula to check
* @returns for each state the probability that the path formula holds
*/
virtual std::vector<Type>* checkBoundedEventually(const storm::formula::BoundedEventually<Type>& formula, bool qualitative) const {
// Create equivalent temporary bounded until formula and check it.
storm::formula::BoundedUntil<Type> temporaryBoundedUntilFormula(new storm::formula::Ap<Type>("true"), formula.getChild().clone(), formula.getBound());
return this->checkBoundedUntil(temporaryBoundedUntilFormula, qualitative);
}
/*!
* The check method for a path formula with an Eventually operator node as root in its formula tree
*
* @param formula The Eventually path formula to check
* @returns for each state the probability that the path formula holds
*/
virtual std::vector<Type>* checkEventually(const storm::formula::Eventually<Type>& formula, bool qualitative) const {
// Create equivalent temporary until formula and check it.
storm::formula::Until<Type> temporaryUntilFormula(new storm::formula::Ap<Type>("true"), formula.getChild().clone());
return this->checkUntil(temporaryUntilFormula, qualitative);
}
/*!
* The check method for a path formula with a Globally operator node as root in its formula tree
*
* @param formula The Globally path formula to check
* @returns for each state the probability that the path formula holds
*/
virtual std::vector<Type>* checkGlobally(const storm::formula::Globally<Type>& formula, bool qualitative) const {
// Create "equivalent" temporary eventually formula and check it.
storm::formula::Eventually<Type> temporaryEventuallyFormula(new storm::formula::Not<Type>(formula.getChild().clone()));
std::vector<Type>* result = this->checkEventually(temporaryEventuallyFormula, qualitative);
// Now subtract the resulting vector from the constant one vector to obtain final result.
storm::utility::subtractFromConstantOneVector(result);
return result;
}
/*!
* The check method for a path formula with an Until operator node as root in its formula tree
*
* @param formula The Until path formula to check
* @returns for each state the probability that the path formula holds.
*/
virtual std::vector<Type>* checkUntil(const storm::formula::Until<Type>& formula, bool qualitative) const {
// First, we need to compute the states that satisfy the sub-formulas of the until-formula.
storm::storage::BitVector* leftStates = formula.getLeft().check(*this);
storm::storage::BitVector* rightStates = formula.getRight().check(*this);
// Then, we need to identify the states which have to be taken out of the matrix, i.e.
// all states that have probability 0 and 1 of satisfying the until-formula.
storm::storage::BitVector statesWithProbability0(this->getModel().getNumberOfStates());
storm::storage::BitVector statesWithProbability1(this->getModel().getNumberOfStates());
storm::utility::GraphAnalyzer::performProb01(this->getModel(), *leftStates, *rightStates, &statesWithProbability0, &statesWithProbability1);
// Delete intermediate results that are obsolete now.
delete leftStates;
delete rightStates;
// Perform some logging.
LOG4CPLUS_INFO(logger, "Found " << statesWithProbability0.getNumberOfSetBits() << " 'no' states.");
LOG4CPLUS_INFO(logger, "Found " << statesWithProbability1.getNumberOfSetBits() << " 'yes' states.");
storm::storage::BitVector maybeStates = ~(statesWithProbability0 | statesWithProbability1);
LOG4CPLUS_INFO(logger, "Found " << maybeStates.getNumberOfSetBits() << " 'maybe' states.");
// Create resulting vector.
std::vector<Type>* result = new std::vector<Type>(this->getModel().getNumberOfStates());
// Only try to solve system if there are states for which the probability is unknown.
uint_fast64_t maybeStatesSetBitCount = maybeStates.getNumberOfSetBits();
if (maybeStatesSetBitCount > 0 && !qualitative) {
// Now we can eliminate the rows and columns from the original transition probability matrix.
storm::storage::SparseMatrix<Type>* submatrix = this->getModel().getTransitionMatrix()->getSubmatrix(maybeStates);
// Converting the matrix from the fixpoint notation to the form needed for the equation
// system. That is, we go from x = A*x + b to (I-A)x = b.
submatrix->convertToEquationSystem();
// Initialize the x vector with 0.5 for each element. This is the initial guess for
// the iterative solvers. It should be safe as for all 'maybe' states we know that the
// probability is strictly larger than 0.
std::vector<Type> x(maybeStatesSetBitCount, Type(0.5));
// Prepare the right-hand side of the equation system. For entry i this corresponds to
// the accumulated probability of going from state i to some 'yes' state.
std::vector<Type> b(maybeStatesSetBitCount);
this->getModel().getTransitionMatrix()->getConstrainedRowSumVector(maybeStates, statesWithProbability1, &b);
this->solveEquationSystem(*submatrix, x, b);
// Delete the created submatrix.
delete submatrix;
// Set values of resulting vector according to result.
storm::utility::setVectorValues<Type>(result, maybeStates, x);
} else if (qualitative) {
// If we only need a qualitative result, we can safely assume that the results will only be compared to
// bounds which are either 0 or 1. Setting the value to 0.5 is thus safe.
storm::utility::setVectorValues<Type>(result, maybeStates, Type(0.5));
}
// Set values of resulting vector that are known exactly.
storm::utility::setVectorValues<Type>(result, statesWithProbability0, storm::utility::constGetZero<Type>());
storm::utility::setVectorValues<Type>(result, statesWithProbability1, storm::utility::constGetOne<Type>());
return result;
}
/*!
* The check method for a path formula with an Instantaneous Reward operator node as root in its
* formula tree
*
* @param formula The Instantaneous Reward formula to check
* @returns for each state the reward that the instantaneous reward yields
*/
virtual std::vector<Type>* checkInstantaneousReward(const storm::formula::InstantaneousReward<Type>& formula, bool qualitative) const {
// Only compute the result if the model has a state-based reward model.
if (!this->getModel().hasStateRewards()) {
LOG4CPLUS_ERROR(logger, "Missing (state-based) reward model for formula.");
throw storm::exceptions::InvalidPropertyException() << "Missing (state-based) reward model for formula.";
}
// Initialize result to state rewards of the model.
std::vector<Type>* result = new std::vector<Type>(*this->getModel().getStateRewardVector());
// Perform the actual matrix-vector multiplication as long as the bound of the formula is met.
this->performMatrixVectorMultiplication(*this->getModel().getTransitionMatrix(), *result, nullptr, formula.getBound());
// Return result.
return result;
}
/*!
* The check method for a path formula with a Cumulative Reward operator node as root in its
* formula tree
*
* @param formula The Cumulative Reward formula to check
* @returns for each state the reward that the cumulative reward yields
*/
virtual std::vector<Type>* checkCumulativeReward(const storm::formula::CumulativeReward<Type>& formula, bool qualitative) const {
// Only compute the result if the model has at least one reward model.
if (!this->getModel().hasStateRewards() && !this->getModel().hasTransitionRewards()) {
LOG4CPLUS_ERROR(logger, "Missing reward model for formula.");
throw storm::exceptions::InvalidPropertyException() << "Missing reward model for formula.";
}
// Compute the reward vector to add in each step based on the available reward models.
std::vector<Type>* totalRewardVector = nullptr;
if (this->getModel().hasTransitionRewards()) {
totalRewardVector = this->getModel().getTransitionMatrix()->getPointwiseProductRowSumVector(*this->getModel().getTransitionRewardMatrix());
if (this->getModel().hasStateRewards()) {
gmm::add(*this->getModel().getStateRewardVector(), *totalRewardVector);
}
} else {
totalRewardVector = new std::vector<Type>(*this->getModel().getStateRewardVector());
}
std::vector<Type>* result = new std::vector<Type>(*this->getModel().getStateRewardVector());
this->performMatrixVectorMultiplication(*this->getModel().getTransitionMatrix(), *result, totalRewardVector, formula.getBound());
// Delete temporary variables and return result.
delete totalRewardVector;
return result;
}
/*!
* The check method for a path formula with a Reachability Reward operator node as root in its
* formula tree
*
* @param formula The Reachbility Reward formula to check
* @returns for each state the reward that the reachability reward yields
*/
virtual std::vector<Type>* checkReachabilityReward(const storm::formula::ReachabilityReward<Type>& formula, bool qualitative) const {
// Only compute the result if the model has at least one reward model.
if (!this->getModel().hasStateRewards() && !this->getModel().hasTransitionRewards()) {
LOG4CPLUS_ERROR(logger, "Missing reward model for formula. Skipping formula");
throw storm::exceptions::InvalidPropertyException() << "Missing reward model for formula.";
}
// Determine the states for which the target predicate holds.
storm::storage::BitVector* targetStates = formula.getChild().check(*this);
// Determine which states have a reward of infinity by definition.
storm::storage::BitVector infinityStates(this->getModel().getNumberOfStates());
storm::storage::BitVector trueStates(this->getModel().getNumberOfStates(), true);
storm::utility::GraphAnalyzer::performProb1(this->getModel(), trueStates, *targetStates, &infinityStates);
infinityStates.complement();
// Create resulting vector.
std::vector<Type>* result = new std::vector<Type>(this->getModel().getNumberOfStates());
// Check whether there are states for which we have to compute the result.
storm::storage::BitVector maybeStates = ~(*targetStates) & ~infinityStates;
const int maybeStatesSetBitCount = maybeStates.getNumberOfSetBits();
if (maybeStatesSetBitCount > 0) {
// Now we can eliminate the rows and columns from the original transition probability matrix.
storm::storage::SparseMatrix<Type>* submatrix = this->getModel().getTransitionMatrix()->getSubmatrix(maybeStates);
// Converting the matrix from the fixpoint notation to the form needed for the equation
// system. That is, we go from x = A*x + b to (I-A)x = b.
submatrix->convertToEquationSystem();
// Initialize the x vector with 1 for each element. This is the initial guess for
// the iterative solvers.
std::vector<Type> x(maybeStatesSetBitCount, storm::utility::constGetOne<Type>());
// Prepare the right-hand side of the equation system.
std::vector<Type> b(maybeStatesSetBitCount);
if (this->getModel().hasTransitionRewards()) {
// If a transition-based reward model is available, we initialize the right-hand
// side to the vector resulting from summing the rows of the pointwise product
// of the transition probability matrix and the transition reward matrix.
std::vector<Type>* pointwiseProductRowSumVector = this->getModel().getTransitionMatrix()->getPointwiseProductRowSumVector(*this->getModel().getTransitionRewardMatrix());
storm::utility::selectVectorValues(&b, maybeStates, *pointwiseProductRowSumVector);
delete pointwiseProductRowSumVector;
if (this->getModel().hasStateRewards()) {
// If a state-based reward model is also available, we need to add this vector
// as well. As the state reward vector contains entries not just for the states
// that we still consider (i.e. maybeStates), we need to extract these values
// first.
std::vector<Type> subStateRewards(maybeStatesSetBitCount);
storm::utility::selectVectorValues(&subStateRewards, maybeStates, *this->getModel().getStateRewardVector());
gmm::add(subStateRewards, b);
}
} else {
// If only a state-based reward model is available, we take this vector as the
// right-hand side. As the state reward vector contains entries not just for the
// states that we still consider (i.e. maybeStates), we need to extract these values
// first.
storm::utility::selectVectorValues(&b, maybeStates, *this->getModel().getStateRewardVector());
}
this->solveEquationSystem(*submatrix, x, b);
// Set values of resulting vector according to result.
storm::utility::setVectorValues<Type>(result, maybeStates, x);
// Delete temporary matrix and right-hand side.
delete submatrix;
}
// Set values of resulting vector that are known exactly.
storm::utility::setVectorValues(result, *targetStates, storm::utility::constGetZero<Type>());
storm::utility::setVectorValues(result, infinityStates, storm::utility::constGetInfinity<Type>());
// Delete temporary storages and return result.
delete targetStates;
return result;
}
private:
virtual void performMatrixVectorMultiplication(storm::storage::SparseMatrix<Type> const& matrix, std::vector<Type>& vector, std::vector<Type>* summand = nullptr, uint_fast64_t repetitions = 1) const = 0;
virtual void solveEquationSystem(storm::storage::SparseMatrix<Type> const& matrix, std::vector<Type>& vector, std::vector<Type> const& b) const = 0;
};
} //namespace modelChecker
} //namespace storm
#endif /* STORM_MODELCHECKER_DTMCPRCTLMODELCHECKER_H_ */