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Removed guessing of initial scheduler as this was just an idea and not meant to be in master at this point. 

Former-commit-id: 1b74c9936d
tempestpy_adaptions
dehnert 11 years ago
parent
11cc7fc6bc
commit
d8e85ec071
1 changed files with 3 additions and 117 deletions
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  1. 120
      src/modelchecker/prctl/SparseMdpPrctlModelChecker.h

120
src/modelchecker/prctl/SparseMdpPrctlModelChecker.h
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@ -314,7 +314,7 @@ namespace storm {
std::vector<Type> b = this->getModel().getTransitionMatrix().getConstrainedRowSumVector(maybeStates, this->getModel().getNondeterministicChoiceIndices(), statesWithProbability1, submatrix.getRowCount());
// Create vector for results for maybe states.
std::vector<Type> x = this->getInitialValueIterationValues(minimize, submatrix, subNondeterministicChoiceIndices, b, statesWithProbability1, maybeStates);
std::vector<Type> x(maybeStates.getNumberOfSetBits());
// Solve the corresponding system of equations.
if (linearEquationSolver != nullptr) {
@ -523,7 +523,7 @@ namespace storm {
}
// Create vector for results for maybe states.
std::vector<Type> x = this->getInitialValueIterationValues(minimize, submatrix, subNondeterministicChoiceIndices, b, *targetStates, maybeStates);
std::vector<Type> x(maybeStates.getNumberOfSetBits());
// Solve the corresponding system of equations.
if (linearEquationSolver != nullptr) {
@ -625,121 +625,7 @@ namespace storm {
return subNondeterministicChoiceIndices;
}
/*!
* Retrieves the values to be used as the initial values for the value iteration techniques.
*
* @param submatrix The matrix that will be used for value iteration later.
* @param subNondeterministicChoiceIndices A vector indicating which rows represent the nondeterministic choices
* of which state in the system that will be used for value iteration.
* @param rightHandSide The right-hand-side of the equation system used for value iteration.
* @param targetStates A set of target states that is to be reached.
* @param maybeStates A set of states that was selected as the system on which to perform value iteration.
* @param guessedScheduler If not the nullptr, this vector will be filled with the scheduler that was
* derived as a preliminary guess.
* @param distancePairs If not the nullptr, this pair of vectors contains the minimal path distances from
* each state to a target state and a non-target state, respectively.
* @return The initial values to be used for the value iteration for the given system.
*/
std::vector<Type> getInitialValueIterationValues(bool minimize, storm::storage::SparseMatrix<Type> const& submatrix,
std::vector<uint_fast64_t> const& subNondeterministicChoiceIndices,
std::vector<Type> const& rightHandSide,
storm::storage::BitVector const& targetStates,
storm::storage::BitVector const& maybeStates,
std::vector<uint_fast64_t>* guessedScheduler = nullptr,
std::pair<std::vector<Type>, std::vector<Type>>* distancePairs = nullptr) const {
storm::settings::Settings* s = storm::settings::Settings::getInstance();
double precision = s->getOptionByLongName("precision").getArgument(0).getValueAsDouble();
if (s->isSet("useHeuristicPresolve")) {
// Compute both the most probable paths to target states as well as the most probable path to non-target states.
// Note that here target state means a state does not *not* satisfy the property that is to be reached
// if we want to minimize the reachability probability.
std::pair<std::vector<Type>, std::vector<uint_fast64_t>> maxDistancesAndPredecessorsPairToTarget = storm::utility::graph::performDijkstra(this->getModel(),
this->getModel().template getBackwardTransitions<Type>([](Type const& value) -> Type { return value; }),
minimize ? ~(maybeStates | targetStates) : targetStates, &maybeStates);
std::pair<std::vector<Type>, std::vector<uint_fast64_t>> maxDistancesAndPredecessorsPairToNonTarget = storm::utility::graph::performDijkstra(this->getModel(),
this->getModel().template getBackwardTransitions<Type>([](Type const& value) -> Type { return value; }),
minimize ? targetStates : ~(maybeStates | targetStates), &maybeStates);
// Now guess the scheduler that could possibly maximize the probability of reaching the target states.
std::vector<uint_fast64_t> scheduler = this->getSchedulerGuess(maybeStates, maxDistancesAndPredecessorsPairToTarget.first, maxDistancesAndPredecessorsPairToNonTarget.first);
// Now that we have a guessed scheduler, we can compute the reachability probability of the system
// under the given scheduler and take these values as the starting point for value iteration.
std::vector<Type> result(scheduler.size(), Type(0.5));
std::vector<Type> b(scheduler.size());
storm::utility::vector::selectVectorValues(b, scheduler, subNondeterministicChoiceIndices, rightHandSide);
storm::storage::SparseMatrix<Type> A(submatrix.getSubmatrix(scheduler, subNondeterministicChoiceIndices));
A.convertToEquationSystem();
storm::solver::GmmxxLinearEquationSolver<Type> solver;
solver.solveEquationSystem(A, result, b);
// As there are sometimes some very small values in the vector due to numerical solving, we set
// them to zero, because they otherwise require a certain number of value iterations.
for (auto& value : result) {
if (value < precision) {
value = 0;
}
}
// If some of the parameters were given, we fill them with the information that they are supposed
// to contain.
if (guessedScheduler != nullptr) {
*guessedScheduler = std::move(scheduler);
}
if (distancePairs != nullptr) {
distancePairs->first = std::move(maxDistancesAndPredecessorsPairToTarget.first);
distancePairs->second = std::move(maxDistancesAndPredecessorsPairToNonTarget.first);
}
return result;
} else {
// If guessing a scheduler was not requested, we just return the constant zero vector as the
// starting point for value iteration.
return std::vector<Type>(submatrix.getColumnCount());
}
}
/*!
* Guesses a scheduler that possibly maximizes the probabiliy of reaching the target states.
*
* @param maybeStates The states for which the scheduler needs to resolve the nondeterminism.
* @param distancesToTarget Contains the minimal distance of reaching a target state for each state.
* @param distancesToNonTarget Contains the minimal distance of reaching a non-target state for each state.
* @return The scheduler that was guessed based on the given distance information.
*/
std::vector<uint_fast64_t> getSchedulerGuess(storm::storage::BitVector const& maybeStates, std::vector<Type> const& distancesToTarget, std::vector<Type> const& distancesToNonTarget) const {
std::vector<uint_fast64_t> scheduler(maybeStates.getNumberOfSetBits());
// For each of the states we need to resolve the nondeterministic choice based on the information we are given.
Type maxProbability = -storm::utility::constGetInfinity<Type>();
Type currentProbability = 0;
uint_fast64_t currentStateIndex = 0;
for (auto state : maybeStates) {
maxProbability = -storm::utility::constGetInfinity<Type>();
for (uint_fast64_t row = 0, rowEnd = this->getModel().getNondeterministicChoiceIndices()[state + 1] - this->getModel().getNondeterministicChoiceIndices()[state]; row < rowEnd; ++row) {
typename storm::storage::SparseMatrix<Type>::Rows currentRow = this->getModel().getTransitionMatrix().getRow(this->getModel().getNondeterministicChoiceIndices()[state] + row);
currentProbability = 0;
for (auto& transition : currentRow) {
currentProbability += transition.value() * distancesToTarget[transition.column()];
// currentProbability -= transition.value() * (1 - distancesToNonTarget[transition.column()]);
}
if (currentProbability > maxProbability) {
maxProbability = currentProbability;
scheduler[currentStateIndex] = row;
}
}
++currentStateIndex;
}
return scheduler;
}
// An object that is used for solving linear equations and performing matrix-vector multiplication.
std::unique_ptr<storm::solver::AbstractNondeterministicLinearEquationSolver<Type>> linearEquationSolver;

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