#ifndef STORM_UTILITY_GRAPH_H_
#define STORM_UTILITY_GRAPH_H_
#include <set>
#include <limits>
#include "utility/OsDetection.h"
#include "src/storage/sparse/StateType.h"
#include "src/models/symbolic/DeterministicModel.h"
#include "src/models/symbolic/NondeterministicModel.h"
#include "src/models/sparse/DeterministicModel.h"
#include "src/models/sparse/NondeterministicModel.h"
#include "src/utility/constants.h"
#include "src/exceptions/InvalidArgumentException.h"
#include "log4cplus/logger.h"
#include "log4cplus/loggingmacros.h"
extern log4cplus::Logger logger;
namespace storm {
namespace utility {
namespace graph {
/*!
* Performs a forward depth-first search through the underlying graph structure to identify the states that
* are reachable from the given set only passing through a constrained set of states until some target
* have been reached.
*
* @param transitionMatrix The transition relation of the graph structure to search.
* @param initialStates The set of states from which to start the search.
* @param constraintStates The set of states that must not be left.
* @param targetStates The target states that may not be passed.
*/
template<typename T>
storm::storage::BitVector getReachableStates(storm::storage::SparseMatrix<T> const& transitionMatrix, storm::storage::BitVector const& initialStates, storm::storage::BitVector const& constraintStates, storm::storage::BitVector const& targetStates) {
storm::storage::BitVector reachableStates(initialStates);
// Initialize the stack used for the DFS with the states.
std::vector<uint_fast64_t> stack(initialStates.begin(), initialStates.end());
// Perform the actual DFS.
uint_fast64_t currentState = 0;
while (!stack.empty()) {
currentState = stack.back();
stack.pop_back();
for (auto const& successor : transitionMatrix.getRow(currentState)) {
// Only explore the state if the transition was actually there and the successor has not yet
// been visited.
if (successor.getValue() != storm::utility::zero<T>() && !reachableStates.get(successor.getColumn())) {
// If the successor is one of the target states, we need to include it, but must not explore
// it further.
if (targetStates.get(successor.getColumn())) {
reachableStates.set(successor.getColumn());
} else if (constraintStates.get(successor.getColumn())) {
// However, if the state is in the constrained set of states, we need to follow it.
reachableStates.set(successor.getColumn());
stack.push_back(successor.getColumn());
}
}
}
}
return reachableStates;
}
/*!
* Performs a breadth-first search through the underlying graph structure to compute the distance from all
* states to the starting states of the search.
*
* @param transitionMatrix The transition relation of the graph structure to search.
* @param initialStates The set of states from which to start the search.
* @return The distances of each state to the initial states of the sarch.
*/
template<typename T>
std::vector<std::size_t> getDistances(storm::storage::SparseMatrix<T> const& transitionMatrix, storm::storage::BitVector const& initialStates) {
std::vector<std::size_t> distances(transitionMatrix.getRowGroupCount());
std::vector<std::pair<storm::storage::sparse::state_type, std::size_t>> stateQueue;
stateQueue.reserve(transitionMatrix.getRowGroupCount());
storm::storage::BitVector statesInQueue(transitionMatrix.getRowGroupCount());
storm::storage::sparse::state_type currentPosition = 0;
for (auto const& initialState : initialStates) {
stateQueue.emplace_back(initialState, 0);
statesInQueue.set(initialState);
}
// Perform a BFS.
while (currentPosition < stateQueue.size()) {
std::pair<storm::storage::sparse::state_type, std::size_t> const& stateDistancePair = stateQueue[currentPosition];
distances[stateDistancePair.first] = stateDistancePair.second;
for (auto const& successorEntry : transitionMatrix.getRowGroup(stateDistancePair.first)) {
if (!statesInQueue.get(successorEntry.getColumn())) {
stateQueue.emplace_back(successorEntry.getColumn(), stateDistancePair.second + 1);
statesInQueue.set(successorEntry.getColumn());
}
}
++currentPosition;
}
return distances;
}
/*!
* Performs a backward depth-first search trough the underlying graph structure
* of the given model to determine which states of the model have a positive probability
* of satisfying phi until psi. The resulting states are written to the given bit vector.
*
* @param backwardTransitions The reversed transition relation of the graph structure to search.
* @param phiStates A bit vector of all states satisfying phi.
* @param psiStates A bit vector of all states satisfying psi.
* @param useStepBound A flag that indicates whether or not to use the given number of maximal steps for the search.
* @param maximalSteps The maximal number of steps to reach the psi states.
* @return A bit vector with all indices of states that have a probability greater than 0.
*/
template <typename T>
storm::storage::BitVector performProbGreater0(storm::storage::SparseMatrix<T> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, bool useStepBound = false, uint_fast64_t maximalSteps = 0) {
// Prepare the resulting bit vector.
uint_fast64_t numberOfStates = phiStates.size();
storm::storage::BitVector statesWithProbabilityGreater0(numberOfStates);
// Add all psi states as they already satisfy the condition.
statesWithProbabilityGreater0 |= psiStates;
// Initialize the stack used for the DFS with the states.
std::vector<uint_fast64_t> stack(psiStates.begin(), psiStates.end());
// Initialize the stack for the step bound, if the number of steps is bounded.
std::vector<uint_fast64_t> stepStack;
std::vector<uint_fast64_t> remainingSteps;
if (useStepBound) {
stepStack.reserve(numberOfStates);
stepStack.insert(stepStack.begin(), psiStates.getNumberOfSetBits(), maximalSteps);
remainingSteps.resize(numberOfStates);
for (auto state : psiStates) {
remainingSteps[state] = maximalSteps;
}
}
// Perform the actual DFS.
uint_fast64_t currentState, currentStepBound;
while (!stack.empty()) {
currentState = stack.back();
stack.pop_back();
if (useStepBound) {
currentStepBound = stepStack.back();
stepStack.pop_back();
}
for (typename storm::storage::SparseMatrix<T>::const_iterator entryIt = backwardTransitions.begin(currentState), entryIte = backwardTransitions.end(currentState); entryIt != entryIte; ++entryIt) {
if (phiStates[entryIt->getColumn()] && (!statesWithProbabilityGreater0.get(entryIt->getColumn()) || (useStepBound && remainingSteps[entryIt->getColumn()] < currentStepBound - 1))) {
// If we don't have a bound on the number of steps to take, just add the state to the stack.
if (!useStepBound) {
statesWithProbabilityGreater0.set(entryIt->getColumn(), true);
stack.push_back(entryIt->getColumn());
} else if (currentStepBound > 0) {
// If there is at least one more step to go, we need to push the state and the new number of steps.
remainingSteps[entryIt->getColumn()] = currentStepBound - 1;
statesWithProbabilityGreater0.set(entryIt->getColumn(), true);
stack.push_back(entryIt->getColumn());
stepStack.push_back(currentStepBound - 1);
}
}
}
}
// Return result.
return statesWithProbabilityGreater0;
}
/*!
* Computes the set of states of the given model for which all paths lead to
* the given set of target states and only visit states from the filter set
* before. In order to do this, it uses the given set of states that
* characterizes the states that possess at least one path to a target state.
* The results are written to the given bit vector.
*
* @param backwardTransitions The reversed transition relation of the graph structure to search.
* @param phiStates A bit vector of all states satisfying phi.
* @param psiStates A bit vector of all states satisfying psi.
* @param statesWithProbabilityGreater0 A reference to a bit vector of states that possess a positive
* probability mass of satisfying phi until psi.
* @return A bit vector with all indices of states that have a probability greater than 1.
*/
template <typename T>
storm::storage::BitVector performProb1(storm::storage::SparseMatrix<T> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, storm::storage::BitVector const& statesWithProbabilityGreater0) {
storm::storage::BitVector statesWithProbability1 = performProbGreater0(backwardTransitions, ~psiStates, ~statesWithProbabilityGreater0);
statesWithProbability1.complement();
return statesWithProbability1;
}
/*!
* Computes the set of states of the given model for which all paths lead to
* the given set of target states and only visit states from the filter set
* before. In order to do this, it uses the given set of states that
* characterizes the states that possess at least one path to a target state.
* The results are written to the given bit vector.
*
* @param backwardTransitions The reversed transition relation of the graph structure to search.
* @param phiStates A bit vector of all states satisfying phi.
* @param psiStates A bit vector of all states satisfying psi.
* @return A bit vector with all indices of states that have a probability greater than 1.
*/
template <typename T>
storm::storage::BitVector performProb1(storm::storage::SparseMatrix<T> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
storm::storage::BitVector statesWithProbabilityGreater0 = performProbGreater0(backwardTransitions, phiStates, psiStates);
storm::storage::BitVector statesWithProbability1 = performProbGreater0(backwardTransitions, ~psiStates, ~(statesWithProbabilityGreater0));
statesWithProbability1.complement();
return statesWithProbability1;
}
/*!
* Computes the sets of states that have probability 0 or 1, respectively, of satisfying phi until psi in a
* deterministic model.
*
* @param model The model whose graph structure to search.
* @param phiStates The set of all states satisfying phi.
* @param psiStates The set of all states satisfying psi.
* @return A pair of bit vectors such that the first bit vector stores the indices of all states
* with probability 0 and the second stores all indices of states with probability 1.
*/
template <typename T>
static std::pair<storm::storage::BitVector, storm::storage::BitVector> performProb01(storm::models::sparse::DeterministicModel<T> const& model, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
std::pair<storm::storage::BitVector, storm::storage::BitVector> result;
storm::storage::SparseMatrix<T> backwardTransitions = model.getBackwardTransitions();
result.first = performProbGreater0(backwardTransitions, phiStates, psiStates);
result.second = performProb1(backwardTransitions, phiStates, psiStates, result.first);
result.first.complement();
return result;
}
/*!
* Computes the sets of states that have probability 0 or 1, respectively, of satisfying phi until psi in a
* deterministic model.
*
* @param backwardTransitions The backward transitions of the model whose graph structure to search.
* @param phiStates The set of all states satisfying phi.
* @param psiStates The set of all states satisfying psi.
* @return A pair of bit vectors such that the first bit vector stores the indices of all states
* with probability 0 and the second stores all indices of states with probability 1.
*/
template <typename T>
static std::pair<storm::storage::BitVector, storm::storage::BitVector> performProb01(storm::storage::SparseMatrix<T> backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
std::pair<storm::storage::BitVector, storm::storage::BitVector> result;
result.first = performProbGreater0(backwardTransitions, phiStates, psiStates);
result.second = performProb1(backwardTransitions, phiStates, psiStates, result.first);
result.first.complement();
return result;
}
/*!
* Computes the set of states that has a positive probability of reaching psi states after only passing
* through phi states before.
*
* @param model The (symbolic) model for which to compute the set of states.
* @param transitionMatrixBdd The transition matrix of the model as a BDD.
* @param phiStatesBdd The BDD containing all phi states of the model.
* @param psiStatesBdd The BDD containing all psi states of the model.
* @return All states with positive probability.
*/
template <storm::dd::DdType Type>
storm::dd::Dd<Type> performProbGreater0(storm::models::symbolic::DeterministicModel<Type> const& model, storm::dd::Dd<Type> const& transitionMatrixBdd, storm::dd::Dd<Type> const& phiStatesBdd, storm::dd::Dd<Type> const& psiStatesBdd) {
// Initialize environment for backward search.
storm::dd::DdManager<Type> const& manager = model.getManager();
storm::dd::Dd<Type> lastIterationStates = manager.getZero();
storm::dd::Dd<Type> statesWithProbabilityGreater0 = psiStatesBdd;
uint_fast64_t iterations = 0;
while (lastIterationStates != statesWithProbabilityGreater0) {
lastIterationStates = statesWithProbabilityGreater0;
statesWithProbabilityGreater0 = statesWithProbabilityGreater0.swapVariables(model.getRowColumnMetaVariablePairs());
statesWithProbabilityGreater0 &= transitionMatrixBdd;
statesWithProbabilityGreater0 = statesWithProbabilityGreater0.existsAbstract(model.getColumnVariables());
statesWithProbabilityGreater0 &= phiStatesBdd;
statesWithProbabilityGreater0 |= lastIterationStates;
++iterations;
}
return statesWithProbabilityGreater0;
}
/*!
* Computes the sets of states that have probability 0 or 1, respectively, of satisfying phi until psi in a
* deterministic model.
*
* @param model The (symbolic) model for which to compute the set of states.
* @param transitionMatrixBdd The transition matrix of the model as a BDD.
* @param phiStatesBdd The BDD containing all phi states of the model.
* @param psiStatesBdd The BDD containing all psi states of the model.
* @return A pair of DDs that represent all states with probability 0 and 1, respectively.
*/
template <storm::dd::DdType Type>
static std::pair<storm::dd::Dd<Type>, storm::dd::Dd<Type>> performProb01(storm::models::symbolic::DeterministicModel<Type> const& model, storm::dd::Dd<Type> const& phiStatesBdd, storm::dd::Dd<Type> const& psiStatesBdd) {
std::pair<storm::dd::Dd<Type>, storm::dd::Dd<Type>> result;
storm::dd::Dd<Type> transitionMatrixBdd = model.getTransitionMatrix().notZero();
result.first = performProbGreater0(model, transitionMatrixBdd, phiStatesBdd, psiStatesBdd);
result.second = !performProbGreater0(model, transitionMatrixBdd, !psiStatesBdd && model.getReachableStates(), !result.first && model.getReachableStates()) && model.getReachableStates();
result.first = !result.first && model.getReachableStates();
return result;
}
/*!
* Computes the sets of states that have probability greater 0 of satisfying phi until psi under at least
* one possible resolution of non-determinism in a non-deterministic model. Stated differently,
* this means that these states have a probability greater 0 of satisfying phi until psi if the
* scheduler tries to minimize this probability.
*
* @param model The model whose graph structure to search.
* @param backwardTransitions The reversed transition relation of the model.
* @param phiStates The set of all states satisfying phi.
* @param psiStates The set of all states satisfying psi.
* @param useStepBound A flag that indicates whether or not to use the given number of maximal steps for the search.
* @param maximalSteps The maximal number of steps to reach the psi states.
* @return A bit vector that represents all states with probability 0.
*/
template <typename T>
storm::storage::BitVector performProbGreater0E(storm::storage::SparseMatrix<T> const& transitionMatrix, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, storm::storage::SparseMatrix<T> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, bool useStepBound = false, uint_fast64_t maximalSteps = 0) {
size_t numberOfStates = phiStates.size();
// Prepare resulting bit vector.
storm::storage::BitVector statesWithProbabilityGreater0(numberOfStates);
// Add all psi states as the already satisfy the condition.
statesWithProbabilityGreater0 |= psiStates;
// Initialize the stack used for the DFS with the states
std::vector<uint_fast64_t> stack(psiStates.begin(), psiStates.end());
// Initialize the stack for the step bound, if the number of steps is bounded.
std::vector<uint_fast64_t> stepStack;
std::vector<uint_fast64_t> remainingSteps;
if (useStepBound) {
stepStack.reserve(numberOfStates);
stepStack.insert(stepStack.begin(), psiStates.getNumberOfSetBits(), maximalSteps);
remainingSteps.resize(numberOfStates);
for (auto state : psiStates) {
remainingSteps[state] = maximalSteps;
}
}
// Perform the actual DFS.
uint_fast64_t currentState, currentStepBound;
while (!stack.empty()) {
currentState = stack.back();
stack.pop_back();
if (useStepBound) {
currentStepBound = stepStack.back();
stepStack.pop_back();
}
for (typename storm::storage::SparseMatrix<T>::const_iterator entryIt = backwardTransitions.begin(currentState), entryIte = backwardTransitions.end(currentState); entryIt != entryIte; ++entryIt) {
if (phiStates.get(entryIt->getColumn()) && (!statesWithProbabilityGreater0.get(entryIt->getColumn()) || (useStepBound && remainingSteps[entryIt->getColumn()] < currentStepBound - 1))) {
// If we don't have a bound on the number of steps to take, just add the state to the stack.
if (!useStepBound) {
statesWithProbabilityGreater0.set(entryIt->getColumn(), true);
stack.push_back(entryIt->getColumn());
} else if (currentStepBound > 0) {
// If there is at least one more step to go, we need to push the state and the new number of steps.
remainingSteps[entryIt->getColumn()] = currentStepBound - 1;
statesWithProbabilityGreater0.set(entryIt->getColumn(), true);
stack.push_back(entryIt->getColumn());
stepStack.push_back(currentStepBound - 1);
}
}
}
}
return statesWithProbabilityGreater0;
}
template <typename T>
storm::storage::BitVector performProb0A(storm::storage::SparseMatrix<T> const& transitionMatrix, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, storm::storage::SparseMatrix<T> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
storm::storage::BitVector statesWithProbability0 = performProbGreater0E(transitionMatrix, nondeterministicChoiceIndices, backwardTransitions, phiStates, psiStates);
statesWithProbability0.complement();
return statesWithProbability0;
}
/*!
* Computes the sets of states that have probability 0 of satisfying phi until psi under all
* possible resolutions of non-determinism in a non-deterministic model. Stated differently,
* this means that these states have probability 0 of satisfying phi until psi even if the
* scheduler tries to maximize this probability.
*
* @param model The model whose graph structure to search.
* @param backwardTransitions The reversed transition relation of the model.
* @param phiStates The set of all states satisfying phi.
* @param psiStates The set of all states satisfying psi.
* @param useStepBound A flag that indicates whether or not to use the given number of maximal steps for the search.
* @param maximalSteps The maximal number of steps to reach the psi states.
* @return A bit vector that represents all states with probability 0.
*/
template <typename T>
storm::storage::BitVector performProb0A(storm::models::sparse::NondeterministicModel<T> const& model, storm::storage::SparseMatrix<T> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
return performProb0A(model.getTransitionMatrix(), model.getNondeterministicChoiceIndices(), backwardTransitions, phiStates, psiStates);
}
/*!
* Computes the sets of states that have probability 1 of satisfying phi until psi under at least
* one possible resolution of non-determinism in a non-deterministic model. Stated differently,
* this means that these states have probability 1 of satisfying phi until psi if the
* scheduler tries to maximize this probability.
*
* @param model The model whose graph structure to search.
* @param backwardTransitions The reversed transition relation of the model.
* @param phiStates The set of all states satisfying phi.
* @param psiStates The set of all states satisfying psi.
* @return A bit vector that represents all states with probability 1.
*/
template <typename T>
storm::storage::BitVector performProb1E(storm::storage::SparseMatrix<T> const& transitionMatrix, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, storm::storage::SparseMatrix<T> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
size_t numberOfStates = phiStates.size();
// Initialize the environment for the iterative algorithm.
storm::storage::BitVector currentStates(numberOfStates, true);
std::vector<uint_fast64_t> stack;
stack.reserve(numberOfStates);
// Perform the loop as long as the set of states gets larger.
bool done = false;
uint_fast64_t currentState;
while (!done) {
stack.clear();
storm::storage::BitVector nextStates(psiStates);
stack.insert(stack.end(), psiStates.begin(), psiStates.end());
while (!stack.empty()) {
currentState = stack.back();
stack.pop_back();
for (typename storm::storage::SparseMatrix<T>::const_iterator predecessorEntryIt = backwardTransitions.begin(currentState), predecessorEntryIte = backwardTransitions.end(currentState); predecessorEntryIt != predecessorEntryIte; ++predecessorEntryIt) {
if (phiStates.get(predecessorEntryIt->getColumn()) && !nextStates.get(predecessorEntryIt->getColumn())) {
// Check whether the predecessor has only successors in the current state set for one of the
// nondeterminstic choices.
for (uint_fast64_t row = nondeterministicChoiceIndices[predecessorEntryIt->getColumn()]; row < nondeterministicChoiceIndices[predecessorEntryIt->getColumn() + 1]; ++row) {
bool allSuccessorsInCurrentStates = true;
for (typename storm::storage::SparseMatrix<T>::const_iterator successorEntryIt = transitionMatrix.begin(row), successorEntryIte = transitionMatrix.end(row); successorEntryIt != successorEntryIte; ++successorEntryIt) {
if (!currentStates.get(successorEntryIt->getColumn())) {
allSuccessorsInCurrentStates = false;
break;
}
}
// If all successors for a given nondeterministic choice are in the current state set, we
// add it to the set of states for the next iteration and perform a backward search from
// that state.
if (allSuccessorsInCurrentStates) {
nextStates.set(predecessorEntryIt->getColumn(), true);
stack.push_back(predecessorEntryIt->getColumn());
break;
}
}
}
}
}
// Check whether we need to perform an additional iteration.
if (currentStates == nextStates) {
done = true;
} else {
currentStates = std::move(nextStates);
}
}
return currentStates;
}
template <typename T>
std::pair<storm::storage::BitVector, storm::storage::BitVector> performProb01Max(storm::storage::SparseMatrix<T> const& transitionMatrix, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, storm::storage::SparseMatrix<T> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
std::pair<storm::storage::BitVector, storm::storage::BitVector> result;
result.first = performProb0A(transitionMatrix, nondeterministicChoiceIndices, backwardTransitions, phiStates, psiStates);
result.second = performProb1E(transitionMatrix, nondeterministicChoiceIndices, backwardTransitions, phiStates, psiStates);
return result;
}
/*!
* Computes the sets of states that have probability 0 or 1, respectively, of satisfying phi
* until psi in a non-deterministic model in which all non-deterministic choices are resolved
* such that the probability is maximized.
*
* @param model The model whose graph structure to search.
* @param phiStates The set of all states satisfying phi.
* @param psiStates The set of all states satisfying psi.
* @return A pair of bit vectors that represent all states with probability 0 and 1, respectively.
*/
template <typename T>
std::pair<storm::storage::BitVector, storm::storage::BitVector> performProb01Max(storm::models::sparse::NondeterministicModel<T> const& model, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
return performProb01Max(model.getTransitionMatrix(), model.getTransitionMatrix().getRowGroupIndices(), model.getBackwardTransitions(), phiStates, psiStates);
}
/*!
* Computes the sets of states that have probability greater 0 of satisfying phi until psi under any
* possible resolution of non-determinism in a non-deterministic model. Stated differently,
* this means that these states have a probability greater 0 of satisfying phi until psi if the
* scheduler tries to maximize this probability.
*
* @param model The model whose graph structure to search.
* @param backwardTransitions The reversed transition relation of the model.
* @param phiStates The set of all states satisfying phi.
* @param psiStates The set of all states satisfying psi.
* @param useStepBound A flag that indicates whether or not to use the given number of maximal steps for the search.
* @param maximalSteps The maximal number of steps to reach the psi states.
* @return A bit vector that represents all states with probability 0.
*/
template <typename T>
storm::storage::BitVector performProbGreater0A(storm::storage::SparseMatrix<T> const& transitionMatrix, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, storm::storage::SparseMatrix<T> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates, bool useStepBound = false, uint_fast64_t maximalSteps = 0) {
size_t numberOfStates = phiStates.size();
// Prepare resulting bit vector.
storm::storage::BitVector statesWithProbabilityGreater0(numberOfStates);
// Add all psi states as the already satisfy the condition.
statesWithProbabilityGreater0 |= psiStates;
// Initialize the stack used for the DFS with the states
std::vector<uint_fast64_t> stack(psiStates.begin(), psiStates.end());
// Initialize the stack for the step bound, if the number of steps is bounded.
std::vector<uint_fast64_t> stepStack;
std::vector<uint_fast64_t> remainingSteps;
if (useStepBound) {
stepStack.reserve(numberOfStates);
stepStack.insert(stepStack.begin(), psiStates.getNumberOfSetBits(), maximalSteps);
remainingSteps.resize(numberOfStates);
for (auto state : psiStates) {
remainingSteps[state] = maximalSteps;
}
}
// Perform the actual DFS.
uint_fast64_t currentState, currentStepBound;
while(!stack.empty()) {
currentState = stack.back();
stack.pop_back();
if (useStepBound) {
currentStepBound = stepStack.back();
stepStack.pop_back();
}
for(typename storm::storage::SparseMatrix<T>::const_iterator predecessorEntryIt = backwardTransitions.begin(currentState), predecessorEntryIte = backwardTransitions.end(currentState); predecessorEntryIt != predecessorEntryIte; ++predecessorEntryIt) {
if (phiStates.get(predecessorEntryIt->getColumn()) && (!statesWithProbabilityGreater0.get(predecessorEntryIt->getColumn()) || (useStepBound && remainingSteps[predecessorEntryIt->getColumn()] < currentStepBound - 1))) {
// Check whether the predecessor has at least one successor in the current state set for every
// nondeterministic choice.
bool addToStatesWithProbabilityGreater0 = true;
for (uint_fast64_t row = nondeterministicChoiceIndices[predecessorEntryIt->getColumn()]; row < nondeterministicChoiceIndices[predecessorEntryIt->getColumn() + 1]; ++row) {
bool hasAtLeastOneSuccessorWithProbabilityGreater0 = false;
for (typename storm::storage::SparseMatrix<T>::const_iterator successorEntryIt = transitionMatrix.begin(row), successorEntryIte = transitionMatrix.end(row); successorEntryIt != successorEntryIte; ++successorEntryIt) {
if (statesWithProbabilityGreater0.get(successorEntryIt->getColumn())) {
hasAtLeastOneSuccessorWithProbabilityGreater0 = true;
break;
}
}
if (!hasAtLeastOneSuccessorWithProbabilityGreater0) {
addToStatesWithProbabilityGreater0 = false;
break;
}
}
// If we need to add the state, then actually add it and perform further search from the state.
if (addToStatesWithProbabilityGreater0) {
// If we don't have a bound on the number of steps to take, just add the state to the stack.
if (!useStepBound) {
statesWithProbabilityGreater0.set(predecessorEntryIt->getColumn(), true);
stack.push_back(predecessorEntryIt->getColumn());
} else if (currentStepBound > 0) {
// If there is at least one more step to go, we need to push the state and the new number of steps.
remainingSteps[predecessorEntryIt->getColumn()] = currentStepBound - 1;
statesWithProbabilityGreater0.set(predecessorEntryIt->getColumn(), true);
stack.push_back(predecessorEntryIt->getColumn());
stepStack.push_back(currentStepBound - 1);
}
}
}
}
}
return statesWithProbabilityGreater0;
}
/*!
* Computes the sets of states that have probability 0 of satisfying phi until psi under at least
* one possible resolution of non-determinism in a non-deterministic model. Stated differently,
* this means that these states have probability 0 of satisfying phi until psi if the
* scheduler tries to minimize this probability.
*
* @param model The model whose graph structure to search.
* @param backwardTransitions The reversed transition relation of the model.
* @param phiStates The set of all states satisfying phi.
* @param psiStates The set of all states satisfying psi.
* @return A bit vector that represents all states with probability 0.
*/
template <typename T>
storm::storage::BitVector performProb0E(storm::models::sparse::NondeterministicModel<T> const& model, storm::storage::SparseMatrix<T> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
storm::storage::BitVector statesWithProbability0 = performProbGreater0A(model.getTransitionMatrix(), model.getNondeterministicChoiceIndices(), backwardTransitions, phiStates, psiStates);
statesWithProbability0.complement();
return statesWithProbability0;
}
template <typename T>
storm::storage::BitVector performProb0E(storm::storage::SparseMatrix<T> const& transitionMatrix, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, storm::storage::SparseMatrix<T> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
storm::storage::BitVector statesWithProbability0 = performProbGreater0A(transitionMatrix, nondeterministicChoiceIndices, backwardTransitions, phiStates, psiStates);
statesWithProbability0.complement();
return statesWithProbability0;
}
/*!
* Computes the sets of states that have probability 1 of satisfying phi until psi under all
* possible resolutions of non-determinism in a non-deterministic model. Stated differently,
* this means that these states have probability 1 of satisfying phi until psi even if the
* scheduler tries to minimize this probability.
*
* @param model The model whose graph structure to search.
* @param backwardTransitions The reversed transition relation of the model.
* @param phiStates The set of all states satisfying phi.
* @param psiStates The set of all states satisfying psi.
* @return A bit vector that represents all states with probability 0.
*/
template <typename T>
storm::storage::BitVector performProb1A( storm::storage::SparseMatrix<T> const& transitionMatrix, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, storm::storage::SparseMatrix<T> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
size_t numberOfStates = phiStates.size();
// Initialize the environment for the iterative algorithm.
storm::storage::BitVector currentStates(numberOfStates, true);
std::vector<uint_fast64_t> stack;
stack.reserve(numberOfStates);
// Perform the loop as long as the set of states gets smaller.
bool done = false;
uint_fast64_t currentState;
while (!done) {
stack.clear();
storm::storage::BitVector nextStates(psiStates);
stack.insert(stack.end(), psiStates.begin(), psiStates.end());
while (!stack.empty()) {
currentState = stack.back();
stack.pop_back();
for(typename storm::storage::SparseMatrix<T>::const_iterator predecessorEntryIt = backwardTransitions.begin(currentState), predecessorEntryIte = backwardTransitions.end(currentState); predecessorEntryIt != predecessorEntryIte; ++predecessorEntryIt) {
if (phiStates.get(predecessorEntryIt->getColumn()) && !nextStates.get(predecessorEntryIt->getColumn())) {
// Check whether the predecessor has only successors in the current state set for all of the
// nondeterminstic choices.
bool allSuccessorsInCurrentStatesForAllChoices = true;
for (typename storm::storage::SparseMatrix<T>::const_iterator successorEntryIt = transitionMatrix.begin(nondeterministicChoiceIndices[predecessorEntryIt->getColumn()]), successorEntryIte = transitionMatrix.begin(nondeterministicChoiceIndices[predecessorEntryIt->getColumn() + 1]); successorEntryIt != successorEntryIte; ++successorEntryIt) {
if (!currentStates.get(successorEntryIt->getColumn())) {
allSuccessorsInCurrentStatesForAllChoices = false;
goto afterCheckLoop;
}
}
afterCheckLoop:
// If all successors for all nondeterministic choices are in the current state set, we
// add it to the set of states for the next iteration and perform a backward search from
// that state.
if (allSuccessorsInCurrentStatesForAllChoices) {
nextStates.set(predecessorEntryIt->getColumn(), true);
stack.push_back(predecessorEntryIt->getColumn());
}
}
}
}
// Check whether we need to perform an additional iteration.
if (currentStates == nextStates) {
done = true;
} else {
currentStates = std::move(nextStates);
}
}
return currentStates;
}
template <typename T>
std::pair<storm::storage::BitVector, storm::storage::BitVector> performProb01Min(storm::storage::SparseMatrix<T> const& transitionMatrix, std::vector<uint_fast64_t> const& nondeterministicChoiceIndices, storm::storage::SparseMatrix<T> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
std::pair<storm::storage::BitVector, storm::storage::BitVector> result;
result.first = performProb0E(transitionMatrix, nondeterministicChoiceIndices, backwardTransitions, phiStates, psiStates);
result.second = performProb1A(transitionMatrix, nondeterministicChoiceIndices, backwardTransitions, phiStates, psiStates);
return result;
}
/*!
* Computes the sets of states that have probability 0 or 1, respectively, of satisfying phi
* until psi in a non-deterministic model in which all non-deterministic choices are resolved
* such that the probability is minimized.
*
* @param model The model whose graph structure to search.
* @param phiStates The set of all states satisfying phi.
* @param psiStates The set of all states satisfying psi.
* @return A pair of bit vectors that represent all states with probability 0 and 1, respectively.
*/
template <typename T>
std::pair<storm::storage::BitVector, storm::storage::BitVector> performProb01Min(storm::models::sparse::NondeterministicModel<T> const& model, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) {
return performProb01Min(model.getTransitionMatrix(), model.getTransitionMatrix().getRowGroupIndices(), model.getBackwardTransitions(), phiStates, psiStates);
}
/*!
* Computes the set of states for which there exists a scheduler that achieves a probability greater than
* zero of satisfying phi until psi.
*
* @param model The (symbolic) model for which to compute the set of states.
* @param transitionMatrixBdd The transition matrix of the model as a BDD.
* @param phiStatesBdd The BDD containing all phi states of the model.
* @param psiStatesBdd The BDD containing all psi states of the model.
* @return A DD representing all such states.
*/
template <storm::dd::DdType Type>
storm::dd::Dd<Type> performProbGreater0E(storm::models::symbolic::NondeterministicModel<Type> const& model, storm::dd::Dd<Type> const& transitionMatrixBdd, storm::dd::Dd<Type> const& phiStatesBdd, storm::dd::Dd<Type> const& psiStatesBdd) {
// Initialize environment for backward search.
storm::dd::DdManager<Type> const& manager = model.getManager();
storm::dd::Dd<Type> lastIterationStates = manager.getZero();
storm::dd::Dd<Type> statesWithProbabilityGreater0E = psiStatesBdd;
uint_fast64_t iterations = 0;
storm::dd::Dd<Type> abstractedTransitionMatrixBdd = transitionMatrixBdd.existsAbstract(model.getNondeterminismVariables());
while (lastIterationStates != statesWithProbabilityGreater0E) {
lastIterationStates = statesWithProbabilityGreater0E;
statesWithProbabilityGreater0E = statesWithProbabilityGreater0E.swapVariables(model.getRowColumnMetaVariablePairs());
statesWithProbabilityGreater0E = statesWithProbabilityGreater0E.andExists(abstractedTransitionMatrixBdd, model.getColumnVariables());
statesWithProbabilityGreater0E &= phiStatesBdd;
statesWithProbabilityGreater0E |= lastIterationStates;
++iterations;
}
return statesWithProbabilityGreater0E;
}
/*!
* Computes the set of states for which there does not exist a scheduler that achieves a probability greater
* than zero of satisfying phi until psi.
*
* @param model The (symbolic) model for which to compute the set of states.
* @param transitionMatrixBdd The transition matrix of the model as a BDD.
* @param phiStates The phi states of the model.
* @param psiStates The psi states of the model.
* @return A DD representing all such states.
*/
template <storm::dd::DdType Type>
storm::dd::Dd<Type> performProb0A(storm::models::symbolic::NondeterministicModel<Type> const& model, storm::dd::Dd<Type> const& transitionMatrixBdd, storm::dd::Dd<Type> const& phiStatesBdd, storm::dd::Dd<Type> const& psiStatesBdd) {
return !performProbGreater0E(model, transitionMatrixBdd, phiStatesBdd, psiStatesBdd) && model.getReachableStates();
}
/*!
* Computes the set of states for which all schedulers achieve a probability greater than zero of satisfying
* phi until psi.
*
* @param model The (symbolic) model for which to compute the set of states.
* @param transitionMatrixBdd The transition matrix of the model as a BDD.
* @param phiStatesBdd The BDD containing all phi states of the model.
* @param psiStatesBdd The BDD containing all psi states of the model.
* @return A DD representing all such states.
*/
template <storm::dd::DdType Type>
storm::dd::Dd<Type> performProbGreater0A(storm::models::symbolic::NondeterministicModel<Type> const& model, storm::dd::Dd<Type> const& transitionMatrixBdd, storm::dd::Dd<Type> const& phiStatesBdd, storm::dd::Dd<Type> const& psiStatesBdd) {
// Initialize environment for backward search.
storm::dd::DdManager<Type> const& manager = model.getManager();
storm::dd::Dd<Type> lastIterationStates = manager.getZero();
storm::dd::Dd<Type> statesWithProbabilityGreater0A = psiStatesBdd;
uint_fast64_t iterations = 0;
while (lastIterationStates != statesWithProbabilityGreater0A) {
lastIterationStates = statesWithProbabilityGreater0A;
statesWithProbabilityGreater0A = statesWithProbabilityGreater0A.swapVariables(model.getRowColumnMetaVariablePairs());
statesWithProbabilityGreater0A = statesWithProbabilityGreater0A.andExists(transitionMatrixBdd, model.getColumnVariables());
statesWithProbabilityGreater0A |= model.getIllegalMask();
statesWithProbabilityGreater0A = statesWithProbabilityGreater0A.universalAbstract(model.getNondeterminismVariables());
statesWithProbabilityGreater0A &= phiStatesBdd;
statesWithProbabilityGreater0A |= psiStatesBdd;
++iterations;
}
return statesWithProbabilityGreater0A;
}
/*!
* Computes the set of states for which there exists a scheduler that achieves probability zero of satisfying
* phi until psi.
*
* @param model The (symbolic) model for which to compute the set of states.
* @param transitionMatrixBdd The transition matrix of the model as a BDD.
* @param phiStatesBdd The BDD containing all phi states of the model.
* @param psiStatesBdd The BDD containing all psi states of the model.
* @return A DD representing all such states.
*/
template <storm::dd::DdType Type>
storm::dd::Dd<Type> performProb0E(storm::models::symbolic::NondeterministicModel<Type> const& model, storm::dd::Dd<Type> const& transitionMatrixBdd, storm::dd::Dd<Type> const& phiStatesBdd, storm::dd::Dd<Type> const& psiStatesBdd) {
return !performProbGreater0A(model, transitionMatrixBdd, phiStatesBdd, psiStatesBdd) && model.getReachableStates();
}
/*!
* Computes the set of states for which all schedulers achieve probability one of satisfying phi until psi.
*
* @param model The (symbolic) model for which to compute the set of states.
* @param transitionMatrixBdd The transition matrix of the model as a BDD.
* @param phiStatesBdd The BDD containing all phi states of the model.
* @param psiStatesBdd The BDD containing all psi states of the model.
* @param statesWithProbabilityGreater0A The states of the model that have a probability greater zero under
* all schedulers.
* @return A DD representing all such states.
*/
template <storm::dd::DdType Type>
storm::dd::Dd<Type> performProb1A(storm::models::symbolic::NondeterministicModel<Type> const& model, storm::dd::Dd<Type> const& transitionMatrixBdd, storm::dd::Dd<Type> const& phiStatesBdd, storm::dd::Dd<Type> const& psiStatesBdd, storm::dd::Dd<Type> const& statesWithProbabilityGreater0A) {
// Initialize environment for backward search.
storm::dd::DdManager<Type> const& manager = model.getManager();
storm::dd::Dd<Type> lastIterationStates = manager.getZero();
storm::dd::Dd<Type> statesWithProbability1A = psiStatesBdd || statesWithProbabilityGreater0A;
uint_fast64_t iterations = 0;
while (lastIterationStates != statesWithProbability1A) {
lastIterationStates = statesWithProbability1A;
statesWithProbability1A = statesWithProbability1A.swapVariables(model.getRowColumnMetaVariablePairs());
statesWithProbability1A = transitionMatrixBdd.implies(statesWithProbability1A).universalAbstract(model.getColumnVariables());
statesWithProbability1A |= model.getIllegalMask();
statesWithProbability1A = statesWithProbability1A.universalAbstract(model.getNondeterminismVariables());
statesWithProbability1A &= statesWithProbabilityGreater0A;
statesWithProbability1A |= psiStatesBdd;
++iterations;
}
return statesWithProbability1A;
}
/*!
* Computes the set of states for which there exists a scheduler that achieves probability one of satisfying
* phi until psi.
*
* @param model The (symbolic) model for which to compute the set of states.
* @param transitionMatrixBdd The transition matrix of the model as a BDD.
* @param phiStatesBdd The BDD containing all phi states of the model.
* @param psiStatesBdd The BDD containing all psi states of the model.
* @param statesWithProbabilityGreater0E The states of the model that have a scheduler that achieves a value
* greater than zero.
* @return A DD representing all such states.
*/
template <storm::dd::DdType Type>
storm::dd::Dd<Type> performProb1E(storm::models::symbolic::NondeterministicModel<Type> const& model, storm::dd::Dd<Type> const& transitionMatrixBdd, storm::dd::Dd<Type> const& phiStatesBdd, storm::dd::Dd<Type> const& psiStatesBdd, storm::dd::Dd<Type> const& statesWithProbabilityGreater0E) {
// Initialize environment for backward search.
storm::dd::DdManager<Type> const& manager = model.getManager();
storm::dd::Dd<Type> statesWithProbability1E = statesWithProbabilityGreater0E;
uint_fast64_t iterations = 0;
bool outerLoopDone = false;
while (!outerLoopDone) {
storm::dd::Dd<Type> innerStates = manager.getZero();
bool innerLoopDone = false;
while (!innerLoopDone) {
storm::dd::Dd<Type> temporary = statesWithProbability1E.swapVariables(model.getRowColumnMetaVariablePairs());
temporary = transitionMatrixBdd.implies(temporary).universalAbstract(model.getColumnVariables());
storm::dd::Dd<Type> temporary2 = innerStates.swapVariables(model.getRowColumnMetaVariablePairs());
temporary2 = transitionMatrixBdd.andExists(temporary2, model.getColumnVariables());
temporary = temporary.andExists(temporary2, model.getNondeterminismVariables());
temporary &= phiStatesBdd;
temporary |= psiStatesBdd;
if (innerStates == temporary) {
innerLoopDone = true;
} else {
innerStates = temporary;
}
}
if (statesWithProbability1E == innerStates) {
outerLoopDone = true;
} else {
statesWithProbability1E = innerStates;
}
++iterations;
}
return statesWithProbability1E;
}
template <storm::dd::DdType Type>
std::pair<storm::dd::Dd<Type>, storm::dd::Dd<Type>> performProb01Max(storm::models::symbolic::NondeterministicModel<Type> const& model, storm::dd::Dd<Type> const& phiStatesBdd, storm::dd::Dd<Type> const& psiStatesBdd) {
std::pair<storm::dd::Dd<Type>, storm::dd::Dd<Type>> result;
storm::dd::Dd<Type> transitionMatrixBdd = model.getTransitionMatrix().notZero();
result.first = performProb0A(model, transitionMatrixBdd, phiStatesBdd, psiStatesBdd);
result.second = performProb1E(model, transitionMatrixBdd, phiStatesBdd, psiStatesBdd, !result.first && model.getReachableStates());
return result;
}
template <storm::dd::DdType Type>
std::pair<storm::dd::Dd<Type>, storm::dd::Dd<Type>> performProb01Min(storm::models::symbolic::NondeterministicModel<Type> const& model, storm::dd::Dd<Type> const& phiStatesBdd, storm::dd::Dd<Type> const& psiStatesBdd) {
std::pair<storm::dd::Dd<Type>, storm::dd::Dd<Type>> result;
storm::dd::Dd<Type> transitionMatrixBdd = model.getTransitionMatrix().notZero();
result.first = performProb0E(model, transitionMatrixBdd, phiStatesBdd, psiStatesBdd);
result.second = performProb1A(model, transitionMatrixBdd, phiStatesBdd, psiStatesBdd, !result.first && model.getReachableStates());
return result;
}
/*!
* Performs a topological sort of the states of the system according to the given transitions.
*
* @param matrix A square matrix representing the transition relation of the system.
* @return A vector of indices that is a topological sort of the states.
*/
template <typename T>
std::vector<uint_fast64_t> getTopologicalSort(storm::storage::SparseMatrix<T> const& matrix) {
if (matrix.getRowCount() != matrix.getColumnCount()) {
LOG4CPLUS_ERROR(logger, "Provided matrix is required to be square.");
throw storm::exceptions::InvalidArgumentException() << "Provided matrix is required to be square.";
}
uint_fast64_t numberOfStates = matrix.getRowCount();
// Prepare the result. This relies on the matrix being square.
std::vector<uint_fast64_t> topologicalSort;
topologicalSort.reserve(numberOfStates);
// Prepare the stacks needed for recursion.
std::vector<uint_fast64_t> recursionStack;
recursionStack.reserve(matrix.getRowCount());
std::vector<typename storm::storage::SparseMatrix<T>::const_iterator> iteratorRecursionStack;
iteratorRecursionStack.reserve(numberOfStates);
// Perform a depth-first search over the given transitions and record states in the reverse order they were visited.
storm::storage::BitVector visitedStates(numberOfStates);
for (uint_fast64_t state = 0; state < numberOfStates; ++state) {
if (!visitedStates.get(state)) {
recursionStack.push_back(state);
iteratorRecursionStack.push_back(matrix.begin(state));
recursionStepForward:
while (!recursionStack.empty()) {
uint_fast64_t currentState = recursionStack.back();
typename storm::storage::SparseMatrix<T>::const_iterator successorIterator = iteratorRecursionStack.back();
visitedStates.set(currentState, true);
recursionStepBackward:
for (; successorIterator != matrix.end(currentState); ++successorIterator) {
if (!visitedStates.get(successorIterator->getColumn())) {
// Put unvisited successor on top of our recursion stack and remember that.
recursionStack.push_back(successorIterator->getColumn());
// Also, put initial value for iterator on corresponding recursion stack.
iteratorRecursionStack.push_back(matrix.begin(successorIterator->getColumn()));
goto recursionStepForward;
}
}
topologicalSort.push_back(currentState);
// If we reach this point, we have completed the recursive descent for the current state.
// That is, we need to pop it from the recursion stacks.
recursionStack.pop_back();
iteratorRecursionStack.pop_back();
// If there is at least one state under the current one in our recursion stack, we need
// to restore the topmost state as the current state and jump to the part after the
// original recursive call.
if (recursionStack.size() > 0) {
currentState = recursionStack.back();
successorIterator = iteratorRecursionStack.back();
goto recursionStepBackward;
}
}
}
}
return topologicalSort;
}
/*!
* A class needed to compare the distances for two states in the Dijkstra search.
*/
template<typename T>
struct DistanceCompare {
bool operator()(std::pair<T, uint_fast64_t> const& lhs, std::pair<T, uint_fast64_t> const& rhs) const {
return lhs.first > rhs.first || (lhs.first == rhs.first && lhs.second > rhs.second);
}
};
/*!
* Performs a Dijkstra search from the given starting states to determine the most probable paths to all other states
* by only passing through the given state set.
*
* @param model The model whose state space is to be searched.
* @param transitions The transitions wrt to which to compute the most probable paths.
* @param startingStates The starting states of the Dijkstra search.
* @param filterStates A set of states that must not be left on any path.
*/
template <typename T>
std::pair<std::vector<T>, std::vector<uint_fast64_t>> performDijkstra(storm::models::sparse::Model<T> const& model,
storm::storage::SparseMatrix<T> const& transitions,
storm::storage::BitVector const& startingStates,
storm::storage::BitVector const* filterStates = nullptr) {
LOG4CPLUS_INFO(logger, "Performing Dijkstra search.");
const uint_fast64_t noPredecessorValue = storm::utility::zero<uint_fast64_t>();
std::vector<T> probabilities(model.getNumberOfStates(), storm::utility::zero<T>());
std::vector<uint_fast64_t> predecessors(model.getNumberOfStates(), noPredecessorValue);
// Set the probability to 1 for all starting states.
std::set<std::pair<T, uint_fast64_t>, DistanceCompare<T>> probabilityStateSet;
for (auto state : startingStates) {
probabilityStateSet.emplace(storm::utility::one<T>(), state);
probabilities[state] = storm::utility::one<T>();
}
// As long as there is one reachable state, we need to consider it.
while (!probabilityStateSet.empty()) {
// Get the state with the least distance from the set and remove it.
std::pair<T, uint_fast64_t> probabilityStatePair = *probabilityStateSet.begin();
probabilityStateSet.erase(probabilityStateSet.begin());
// Now check the new distances for all successors of the current state.
typename storm::storage::SparseMatrix<T>::Rows row = transitions.getRow(probabilityStatePair.second);
for (auto const& transition : row) {
// Only follow the transition if it lies within the filtered states.
if (filterStates != nullptr && filterStates->get(transition.first)) {
// Calculate the distance we achieve when we take the path to the successor via the current state.
T newDistance = probabilityStatePair.first * transition.second;
// We found a cheaper way to get to the target state of the transition.
if (newDistance > probabilities[transition.first]) {
// Remove the old distance.
if (probabilities[transition.first] != noPredecessorValue) {
probabilityStateSet.erase(std::make_pair(probabilities[transition.first], transition.first));
}
// Set and add the new distance.
probabilities[transition.first] = newDistance;
predecessors[transition.first] = probabilityStatePair.second;
probabilityStateSet.insert(std::make_pair(newDistance, transition.first));
}
}
}
}
// Move the values into the result and return it.
std::pair<std::vector<T>, std::vector<uint_fast64_t>> result;
result.first = std::move(probabilities);
result.second = std::move(predecessors);
LOG4CPLUS_INFO(logger, "Done performing Dijkstra search.");
return result;
}
} // namespace graph
} // namespace utility
} // namespace storm
#endif /* STORM_UTILITY_GRAPH_H_ */