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fixed issue related to Markov automata without proababilistic states

tempestpy_adaptions
dehnert 7 years ago
parent
0d78367b9a
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f5b1259f3c
1 changed files with 44 additions and 23 deletions
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  1. 67
      src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.cpp

67
src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.cpp
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@ -44,9 +44,16 @@ namespace storm {
// * a matrix aProbabilistic with all (non-discretized) transitions from probabilistic non-goal states to other probabilistic non-goal states.
// * a matrix aProbabilisticToMarkovian with all (non-discretized) transitions from probabilistic non-goal states to all Markovian non-goal states.
typename storm::storage::SparseMatrix<ValueType> aMarkovian = transitionMatrix.getSubmatrix(true, markovianNonGoalStates, markovianNonGoalStates, true);
typename storm::storage::SparseMatrix<ValueType> aMarkovianToProbabilistic = transitionMatrix.getSubmatrix(true, markovianNonGoalStates, probabilisticNonGoalStates);
typename storm::storage::SparseMatrix<ValueType> aProbabilistic = transitionMatrix.getSubmatrix(true, probabilisticNonGoalStates, probabilisticNonGoalStates);
typename storm::storage::SparseMatrix<ValueType> aProbabilisticToMarkovian = transitionMatrix.getSubmatrix(true, probabilisticNonGoalStates, markovianNonGoalStates);
bool existProbabilisticStates = !probabilisticNonGoalStates.empty();
typename storm::storage::SparseMatrix<ValueType> aMarkovianToProbabilistic;
typename storm::storage::SparseMatrix<ValueType> aProbabilistic;
typename storm::storage::SparseMatrix<ValueType> aProbabilisticToMarkovian;
if (existProbabilisticStates) {
aMarkovianToProbabilistic = transitionMatrix.getSubmatrix(true, markovianNonGoalStates, probabilisticNonGoalStates);
aProbabilistic = transitionMatrix.getSubmatrix(true, probabilisticNonGoalStates, probabilisticNonGoalStates);
aProbabilisticToMarkovian = transitionMatrix.getSubmatrix(true, probabilisticNonGoalStates, markovianNonGoalStates);
}
// The matrices with transitions from Markovian states need to be digitized.
// Digitize aMarkovian. Based on whether the transition is a self-loop or not, we apply the two digitization rules.
@ -64,20 +71,25 @@ namespace storm {
}
// Digitize aMarkovianToProbabilistic. As there are no self-loops in this case, we only need to apply the digitization formula for regular successors.
rowIndex = 0;
for (auto state : markovianNonGoalStates) {
for (auto& element : aMarkovianToProbabilistic.getRow(rowIndex)) {
element.setValue((1 - std::exp(-exitRates[state] * delta)) * element.getValue());
if (existProbabilisticStates) {
rowIndex = 0;
for (auto state : markovianNonGoalStates) {
for (auto& element : aMarkovianToProbabilistic.getRow(rowIndex)) {
element.setValue((1 - std::exp(-exitRates[state] * delta)) * element.getValue());
}
++rowIndex;
}
++rowIndex;
}
// Initialize the two vectors that hold the variable one-step probabilities to all target states for probabilistic and Markovian (non-goal) states.
std::vector<ValueType> bProbabilistic(aProbabilistic.getRowCount());
std::vector<ValueType> bProbabilistic(existProbabilisticStates ? aProbabilistic.getRowCount() : 0);
std::vector<ValueType> bMarkovian(markovianNonGoalStates.getNumberOfSetBits());
// Compute the two fixed right-hand side vectors, one for Markovian states and one for the probabilistic ones.
std::vector<ValueType> bProbabilisticFixed = transitionMatrix.getConstrainedRowGroupSumVector(probabilisticNonGoalStates, goalStates);
std::vector<ValueType> bProbabilisticFixed;
if (existProbabilisticStates) {
bProbabilisticFixed = transitionMatrix.getConstrainedRowGroupSumVector(probabilisticNonGoalStates, goalStates);
}
std::vector<ValueType> bMarkovianFixed;
bMarkovianFixed.reserve(markovianNonGoalStates.getNumberOfSetBits());
for (auto state : markovianNonGoalStates) {
@ -110,25 +122,34 @@ namespace storm {
// * perform one timed-step using v_MS := A_MSwG * v_MS + A_MStoPS * v_PS + (A * 1_G)|MS
std::vector<ValueType> markovianNonGoalValuesSwap(markovianNonGoalValues);
for (uint64_t currentStep = 0; currentStep < numberOfSteps; ++currentStep) {
// Start by (re-)computing bProbabilistic = bProbabilisticFixed + aProbabilisticToMarkovian * vMarkovian.
aProbabilisticToMarkovian.multiplyWithVector(markovianNonGoalValues, bProbabilistic);
storm::utility::vector::addVectors(bProbabilistic, bProbabilisticFixed, bProbabilistic);
if (existProbabilisticStates) {
// Start by (re-)computing bProbabilistic = bProbabilisticFixed + aProbabilisticToMarkovian * vMarkovian.
aProbabilisticToMarkovian.multiplyWithVector(markovianNonGoalValues, bProbabilistic);
storm::utility::vector::addVectors(bProbabilistic, bProbabilisticFixed, bProbabilistic);
// Now perform the inner value iteration for probabilistic states.
solver->solveEquations(env, dir, probabilisticNonGoalValues, bProbabilistic);
// Now perform the inner value iteration for probabilistic states.
solver->solveEquations(env, dir, probabilisticNonGoalValues, bProbabilistic);
// (Re-)compute bMarkovian = bMarkovianFixed + aMarkovianToProbabilistic * vProbabilistic.
aMarkovianToProbabilistic.multiplyWithVector(probabilisticNonGoalValues, bMarkovian);
storm::utility::vector::addVectors(bMarkovian, bMarkovianFixed, bMarkovian);
}
// (Re-)compute bMarkovian = bMarkovianFixed + aMarkovianToProbabilistic * vProbabilistic.
aMarkovianToProbabilistic.multiplyWithVector(probabilisticNonGoalValues, bMarkovian);
storm::utility::vector::addVectors(bMarkovian, bMarkovianFixed, bMarkovian);
aMarkovian.multiplyWithVector(markovianNonGoalValues, markovianNonGoalValuesSwap);
std::swap(markovianNonGoalValues, markovianNonGoalValuesSwap);
storm::utility::vector::addVectors(markovianNonGoalValues, bMarkovian, markovianNonGoalValues);
if (existProbabilisticStates) {
storm::utility::vector::addVectors(markovianNonGoalValues, bMarkovian, markovianNonGoalValues);
} else {
storm::utility::vector::addVectors(markovianNonGoalValues, bMarkovianFixed, markovianNonGoalValues);
}
}
// After the loop, perform one more step of the value iteration for PS states.
aProbabilisticToMarkovian.multiplyWithVector(markovianNonGoalValues, bProbabilistic);
storm::utility::vector::addVectors(bProbabilistic, bProbabilisticFixed, bProbabilistic);
solver->solveEquations(env, dir, probabilisticNonGoalValues, bProbabilistic);
if (existProbabilisticStates) {
// After the loop, perform one more step of the value iteration for PS states.
aProbabilisticToMarkovian.multiplyWithVector(markovianNonGoalValues, bProbabilistic);
storm::utility::vector::addVectors(bProbabilistic, bProbabilisticFixed, bProbabilistic);
solver->solveEquations(env, dir, probabilisticNonGoalValues, bProbabilistic);
}
}
template <typename ValueType, typename std::enable_if<!storm::NumberTraits<ValueType>::SupportsExponential, int>::type>

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