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fixed use of FoxLynn after CutOff

tempestpy_adaptions
Timo Philipp Gros 7 years ago
parent
7db58c6374
commit
535a6017e3
1 changed files with 180 additions and 156 deletions
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  1. 336
      src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.cpp

336
src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.cpp
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@ -242,7 +242,7 @@ namespace storm {
if (unifVectors[kind][k][node]!=-1){
logfile << "already calculated for k = " << k << " node = " << node << "\n";
//logfile << "already calculated for k = " << k << " node = " << node << "\n";
return;
}
std::string print = std::string("calculating vector ") + std::to_string(kind) + " for k = " + std::to_string(k) + " node " + std::to_string(node) +" \t";
@ -267,8 +267,10 @@ namespace storm {
// Vd
res = storm::utility::zero<ValueType>();
for (uint64_t i = k ; i<N ; i++){
ValueType between = poisson[i];
res+=between;
if (i < poisson.size()){
ValueType between = poisson[i];
res+=between;
}
}
unifVectors[kind][k][node]=res;
} else {
@ -500,182 +502,204 @@ namespace storm {
}
}
template <typename ValueType, typename std::enable_if<storm::NumberTraits<ValueType>::SupportsExponential, int>::type>
std::vector<ValueType> SparseMarkovAutomatonCslHelper::unifPlus(OptimizationDirection dir, std::pair<double, double> const& boundsPair, std::vector<ValueType> const& exitRateVector, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::BitVector const& markovStates, storm::storage::BitVector const& psiStates, storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const& minMaxLinearEquationSolverFactory){
STORM_LOG_TRACE("Using UnifPlus to compute bounded until probabilities.");
template<typename ValueType, typename std::enable_if<storm::NumberTraits<ValueType>::SupportsExponential, int>::type>
std::vector<ValueType> SparseMarkovAutomatonCslHelper::unifPlus(OptimizationDirection dir,
std::pair<double, double> const &boundsPair,
std::vector<ValueType> const &exitRateVector,
storm::storage::SparseMatrix<ValueType> const &transitionMatrix,
storm::storage::BitVector const &markovStates,
storm::storage::BitVector const &psiStates,
storm::solver::MinMaxLinearEquationSolverFactory<ValueType> const &minMaxLinearEquationSolverFactory) {
STORM_LOG_TRACE("Using UnifPlus to compute bounded until probabilities.");
std::ofstream logfile("U+logfile.txt", std::ios::app);
ValueType maxNorm = storm::utility::zero<ValueType>();
ValueType oldDiff = -storm::utility::zero<ValueType>();
//bitvectors to identify different kind of states
storm::storage::BitVector markovianStates = markovStates;
storm::storage::BitVector allStates(markovianStates.size(), true);
storm::storage::BitVector probabilisticStates = ~markovianStates;
//vectors to save calculation
std::vector<std::vector<ValueType>> vd, vu, wu;
std::vector<std::vector<std::vector<ValueType>>> unifVectors{};
//transitions from goalStates will be ignored. still: they are not allowed to be probabilistic!
for (uint64_t i = 0; i < psiStates.size(); i++) {
if (psiStates[i]) {
markovianStates.set(i, true);
probabilisticStates.set(i, false);
}
}
//transition matrix with diagonal entries. The values can be changed during uniformisation
std::vector<ValueType> exitRate{exitRateVector};
typename storm::storage::SparseMatrix<ValueType> fullTransitionMatrix = transitionMatrix.getSubmatrix(
true, allStates, allStates, true);
// delete diagonals
deleteProbDiagonals(fullTransitionMatrix, markovianStates);
typename storm::storage::SparseMatrix<ValueType> probMatrix{};
uint64_t probSize = 0;
if (probabilisticStates.getNumberOfSetBits() != 0) { //work around in case there are no prob states
probMatrix = fullTransitionMatrix.getSubmatrix(true, probabilisticStates, probabilisticStates,
true);
probSize = probMatrix.getRowCount();
}
auto &rowGroupIndices = fullTransitionMatrix.getRowGroupIndices();
//(1) define horizon, epsilon, kappa , N, lambda,
uint64_t numberOfStates = fullTransitionMatrix.getRowGroupCount();
double T = boundsPair.second;
ValueType kappa = storm::utility::one<ValueType>() / 10; // would be better as option-parameter
ValueType epsilon = storm::settings::getModule<storm::settings::modules::GeneralSettings>().getPrecision();
ValueType lambda = exitRateVector[0];
for (ValueType act: exitRateVector) {
lambda = std::max(act, lambda);
}
uint64_t N;
std::ofstream logfile("U+logfile.txt", std::ios::app);
ValueType maxNorm = storm::utility::zero<ValueType>();
ValueType oldDiff = -storm::utility::zero<ValueType>();
//bitvectors to identify different kind of states
storm::storage::BitVector markovianStates = markovStates;
storm::storage::BitVector allStates(markovianStates.size(), true);
storm::storage::BitVector probabilisticStates = ~markovianStates;
//calculate relative ReachabilityVectors
std::vector<ValueType> in(numberOfStates, 0);
std::vector<std::vector<ValueType>> relReachability(fullTransitionMatrix.getRowCount(), in);
//vectors to save calculation
std::vector<std::vector<ValueType>> vd,vu,wu;
std::vector<std::vector<std::vector<ValueType>>> unifVectors{};
//calculate relative reachability
//transitions from goalStates will be ignored. still: they are not allowed to be probabilistic!
for (uint64_t i =0 ; i<psiStates.size(); i++){
if (psiStates[i]){
markovianStates.set(i,true);
probabilisticStates.set(i,false);
for (uint64_t i = 0; i < numberOfStates; i++) {
if (markovianStates[i]) {
continue;
}
auto from = rowGroupIndices[i];
auto to = rowGroupIndices[i + 1];
for (auto j = from; j < to; j++) {
std::vector<ValueType> &act = relReachability[j];
for (auto element: fullTransitionMatrix.getRow(j)) {
if (markovianStates[element.getColumn()]) {
act[element.getColumn()] = element.getValue();
}
}
//transition matrix with diagonal entries. The values can be changed during uniformisation
std::vector<ValueType> exitRate{exitRateVector};
typename storm::storage::SparseMatrix<ValueType> fullTransitionMatrix = transitionMatrix.getSubmatrix(true, allStates , allStates , true);
// delete diagonals
deleteProbDiagonals(fullTransitionMatrix, markovianStates);
typename storm::storage::SparseMatrix<ValueType> probMatrix{};
uint64_t probSize =0;
if (probabilisticStates.getNumberOfSetBits()!=0){ //work around in case there are no prob states
probMatrix = fullTransitionMatrix.getSubmatrix(true, probabilisticStates , probabilisticStates, true);
probSize = probMatrix.getRowCount();
}
}
auto& rowGroupIndices = fullTransitionMatrix.getRowGroupIndices();
//create equitation solver
storm::solver::MinMaxLinearEquationSolverRequirements requirements = minMaxLinearEquationSolverFactory.getRequirements(
true, dir);
requirements.clearBounds();
STORM_LOG_THROW(requirements.empty(), storm::exceptions::UncheckedRequirementException,
"Cannot establish requirements for solver.");
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> solver;
if (probSize != 0) {
solver = minMaxLinearEquationSolverFactory.create(probMatrix);
solver->setHasUniqueSolution();
solver->setBounds(storm::utility::zero<ValueType>(), storm::utility::one<ValueType>());
solver->setRequirementsChecked();
solver->setCachingEnabled(true);
}
// while not close enough to precision:
do {
maxNorm = storm::utility::zero<ValueType>();
// (2) update parameter
N = ceil(lambda * T * exp(2) - log(kappa * epsilon));
// (3) uniform - just applied to markovian states
for (uint_fast64_t i = 0; i < fullTransitionMatrix.getRowGroupCount(); i++) {
if (!markovianStates[i]) {
continue;
}
uint64_t from = rowGroupIndices[i]; //markovian state -> no Nondeterminism -> only one row
if (exitRate[i] == lambda) {
continue; //already unified
}
//(1) define horizon, epsilon, kappa , N, lambda,
uint64_t numberOfStates = fullTransitionMatrix.getRowGroupCount();
double T = boundsPair.second;
ValueType kappa = storm::utility::one<ValueType>() /10; // would be better as option-parameter
ValueType epsilon = storm::settings::getModule<storm::settings::modules::GeneralSettings>().getPrecision();
ValueType lambda = exitRateVector[0];
for (ValueType act: exitRateVector) {
lambda = std::max(act, lambda);
auto line = fullTransitionMatrix.getRow(from);
ValueType exitOld = exitRate[i];
ValueType exitNew = lambda;
for (auto &v : line) {
if (v.getColumn() == i) { //diagonal element
ValueType newSelfLoop = exitNew - exitOld + v.getValue();
ValueType newRate = newSelfLoop / exitNew;
v.setValue(newRate);
} else { //modify probability
ValueType propOld = v.getValue();
ValueType propNew = propOld * exitOld / exitNew;
v.setValue(propNew);
}
}
exitRate[i] = exitNew;
}
uint64_t N;
// calculate poisson distribution
std::tuple<uint_fast64_t, uint_fast64_t, ValueType, std::vector<ValueType>> foxGlynnResult = storm::utility::numerical::getFoxGlynnCutoff(
T * lambda, 1e+300, epsilon * epsilon * kappa);
//calculate relative ReachabilityVectors
std::vector<ValueType> in(numberOfStates, 0);
std::vector<std::vector<ValueType>> relReachability(fullTransitionMatrix.getRowCount(),in);
// Scale the weights so they add up to one.
for (auto &element : std::get<3>(foxGlynnResult)) {
element /= std::get<2>(foxGlynnResult);
}
// (4) define vectors/matrices
std::vector<ValueType> init(numberOfStates, -1);
vd = std::vector<std::vector<ValueType>>(N + 1, init);
vu = std::vector<std::vector<ValueType>>(N + 1, init);
wu = std::vector<std::vector<ValueType>>(N + 1, init);
unifVectors.clear();
unifVectors.push_back(vd);
unifVectors.push_back(vd);
unifVectors.push_back(vd);
//define 0=vd 1=vu 2=wu
// (5) calculate vectors and maxNorm
for (uint64_t i = 0; i < numberOfStates; i++) {
for (uint64_t k = N; k <= N; k--) {
calculateUnifPlusVector(k, i, 0, lambda, probSize, relReachability, dir, unifVectors,
fullTransitionMatrix, markovianStates, psiStates, solver, logfile,
std::get<3>(foxGlynnResult));
calculateUnifPlusVector(k, i, 2, lambda, probSize, relReachability, dir, unifVectors,
fullTransitionMatrix, markovianStates, psiStates, solver, logfile,
std::get<3>(foxGlynnResult));
calculateVu(relReachability, dir, k, i, 1, lambda, probSize, unifVectors,
fullTransitionMatrix, markovianStates, psiStates, solver, logfile,
std::get<3>(foxGlynnResult));
//also use iteration to keep maxNorm of vd and vup to date, so the loop-condition is easy to prove
ValueType diff = std::abs(unifVectors[0][k][i] - unifVectors[1][k][i]);
maxNorm = std::max(maxNorm, diff);
}
}
printTransitions(N, maxNorm, fullTransitionMatrix, exitRate, markovianStates, psiStates,
relReachability, psiStates, psiStates, unifVectors, logfile); //TODO remove
//calculate relative reachability
// (6) double lambda
for(uint64_t i=0 ; i<numberOfStates; i++){
if (markovianStates[i]){
continue;
}
auto from = rowGroupIndices[i];
auto to = rowGroupIndices[i+1];
for (auto j=from ; j<to; j++){
std::vector<ValueType>& act = relReachability[j];
for(auto element: fullTransitionMatrix.getRow(j)){
if (markovianStates[element.getColumn()]){
act[element.getColumn()]=element.getValue();
}
}
}
}
lambda = 2 * lambda;
//create equitation solver
storm::solver::MinMaxLinearEquationSolverRequirements requirements = minMaxLinearEquationSolverFactory.getRequirements(true, dir);
requirements.clearBounds();
STORM_LOG_THROW(requirements.empty(), storm::exceptions::UncheckedRequirementException, "Cannot establish requirements for solver.");
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> solver;
if (probSize!=0){
solver = minMaxLinearEquationSolverFactory.create(probMatrix);
solver->setHasUniqueSolution();
solver->setBounds(storm::utility::zero<ValueType>(), storm::utility::one<ValueType>());
solver->setRequirementsChecked();
solver->setCachingEnabled(true);
}
// while not close enough to precision:
do {
maxNorm = storm::utility::zero<ValueType>();
// (2) update parameter
N = ceil(lambda*T*exp(2)-log(kappa*epsilon));
// (3) uniform - just applied to markovian states
for (uint_fast64_t i = 0; i < fullTransitionMatrix.getRowGroupCount(); i++) {
if (!markovianStates[i]) {
continue;
}
uint64_t from = rowGroupIndices[i]; //markovian state -> no Nondeterminism -> only one row
if (exitRate[i] == lambda) {
continue; //already unified
}
auto line = fullTransitionMatrix.getRow(from);
ValueType exitOld = exitRate[i];
ValueType exitNew = lambda;
for (auto &v : line) {
if (v.getColumn() == i) { //diagonal element
ValueType newSelfLoop = exitNew - exitOld + v.getValue();
ValueType newRate = newSelfLoop / exitNew;
v.setValue(newRate);
} else { //modify probability
ValueType propOld = v.getValue();
ValueType propNew = propOld * exitOld / exitNew;
v.setValue(propNew);
}
}
exitRate[i] = exitNew;
}
// calculate poisson distribution
std::tuple<uint_fast64_t, uint_fast64_t, ValueType, std::vector<ValueType>> foxGlynnResult = storm::utility::numerical::getFoxGlynnCutoff(T*lambda, 1e+300, epsilon*kappa/ 8.0);
// Scale the weights so they add up to one.
for (auto& element : std::get<3>(foxGlynnResult)) {
element /= std::get<2>(foxGlynnResult);
}
// (4) define vectors/matrices
std::vector<ValueType> init(numberOfStates, -1);
vd = std::vector<std::vector<ValueType>> (N + 1, init);
vu = std::vector<std::vector<ValueType>> (N + 1, init);
wu = std::vector<std::vector<ValueType>> (N + 1, init);
unifVectors.clear();
unifVectors.push_back(vd);
unifVectors.push_back(vd);
unifVectors.push_back(vd);
//define 0=vd 1=vu 2=wu
// (5) calculate vectors and maxNorm
for (uint64_t i = 0; i < numberOfStates; i++) {
for (uint64_t k = N; k <= N; k--) {
calculateUnifPlusVector(k,i,0,lambda,probSize,relReachability,dir,unifVectors,fullTransitionMatrix,markovianStates,psiStates,solver, logfile, std::get<3>(foxGlynnResult));
calculateUnifPlusVector(k,i,2,lambda,probSize,relReachability,dir,unifVectors,fullTransitionMatrix,markovianStates,psiStates,solver, logfile, std::get<3>(foxGlynnResult));
calculateVu(relReachability,dir,k,i,1,lambda,probSize,unifVectors,fullTransitionMatrix,markovianStates,psiStates,solver, logfile, std::get<3>(foxGlynnResult));
//also use iteration to keep maxNorm of vd and vup to date, so the loop-condition is easy to prove
ValueType diff = std::abs(unifVectors[0][k][i]-unifVectors[1][k][i]);
maxNorm = std::max(maxNorm, diff);
}
}
printTransitions(N, maxNorm, fullTransitionMatrix,exitRate,markovianStates,psiStates,relReachability,psiStates, psiStates,unifVectors, logfile); //TODO remove
// (6) double lambda
lambda=2*lambda;
// (7) escape if not coming closer to solution
if (oldDiff!=-1){
if (oldDiff==maxNorm){
std::cout << "Not coming closer to solution as " << maxNorm << "/n";
break;
}
}
oldDiff = maxNorm;
} while (maxNorm>epsilon*(1-kappa));
logfile.close();
return unifVectors[0][0];
// (7) escape if not coming closer to solution
if (oldDiff != -1) {
if (oldDiff == maxNorm) {
std::cout << "Not coming closer to solution as " << maxNorm << "/n";
break;
}
}
oldDiff = maxNorm;
} while (maxNorm > epsilon * (1 - kappa));
logfile.close();
return unifVectors[0][0];
}

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