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Separated LRA tests from CTMC tests and added a testcase for LRA Rewards

tempestpy_adaptions
Tim Quatmann 5 years ago
parent
6110a677f5
commit
f9f845bb79
3 changed files with 411 additions and 14 deletions
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  1. 11
      resources/examples/testfiles/ctmc/lrarewards.sm
  2. 14
      src/test/storm/modelchecker/csl/CtmcCslModelCheckerTest.cpp
  3. 400
      src/test/storm/modelchecker/csl/LraCtmcCslModelCheckerTest.cpp

11
resources/examples/testfiles/ctmc/lrarewards.sm
View File

@ -0,0 +1,11 @@
ctmc
module main
s : [0..3];
[] s < 3 -> 3/2: (s'=s+1);
[] s > 0 -> 3: (s'=s-1);
endmodule
rewards
true: s;
endrewards

14
src/test/storm/modelchecker/csl/CtmcCslModelCheckerTest.cpp
View File

@ -302,7 +302,6 @@ namespace {
formulasString += "; P=? [ !\"minimum\" U>=1 \"minimum\"]";
formulasString += "; P=? [ \"minimum\" U>=1 !\"minimum\"]";
formulasString += "; R=? [C<=100]";
formulasString += "; LRA=? [\"minimum\"]";
auto modelFormulas = this->buildModelFormulas(STORM_TEST_RESOURCES_DIR "/ctmc/cluster2.sm", formulasString);
auto model = std::move(modelFormulas.first);
@ -334,9 +333,6 @@ namespace {
result = checker->check(this->env(), tasks[6]);
EXPECT_NEAR(this->parseNumber("0.8602815057967503"), this->getQuantitativeResultAtInitialState(model, result), this->precision());
result = checker->check(this->env(), tasks[7]);
EXPECT_NEAR(this->parseNumber("0.99999766034263426"), this->getQuantitativeResultAtInitialState(model, result), this->precision());
}
TYPED_TEST(CtmcCslModelCheckerTest, Embedded) {
@ -345,7 +341,6 @@ namespace {
formulasString += "; P=? [ !\"down\" U<=10000 \"fail_io\"]";
formulasString += "; P=? [ !\"down\" U<=10000 \"fail_sensors\"]";
formulasString += "; R{\"up\"}=? [C<=10000]";
formulasString += "; LRA=? [\"fail_sensors\"]";
auto modelFormulas = this->buildModelFormulas(STORM_TEST_RESOURCES_DIR "/ctmc/embedded2.sm", formulasString);
auto model = std::move(modelFormulas.first);
@ -371,13 +366,10 @@ namespace {
result = checker->check(this->env(), tasks[4]);
EXPECT_NEAR(this->parseNumber("2.7745274082080154"), this->getQuantitativeResultAtInitialState(model, result), this->precision());
result = checker->check(this->env(), tasks[5]);
EXPECT_NEAR(this->parseNumber("0.93458866427696596"), this->getQuantitativeResultAtInitialState(model, result), this->precision());
}
TYPED_TEST(CtmcCslModelCheckerTest, Polling) {
std::string formulasString = "P=?[ F<=10 \"target\"]";
formulasString += "; LRA=?[\"target\"]";
auto modelFormulas = this->buildModelFormulas(STORM_TEST_RESOURCES_DIR "/ctmc/polling2.sm", formulasString);
auto model = std::move(modelFormulas.first);
@ -391,8 +383,6 @@ namespace {
result = checker->check(this->env(), tasks[0]);
EXPECT_NEAR(this->parseNumber("1"), this->getQuantitativeResultAtInitialState(model, result), this->precision());
result = checker->check(this->env(), tasks[1]);
EXPECT_NEAR(this->parseNumber("0.20079750055570736"), this->getQuantitativeResultAtInitialState(model, result), this->precision());
}
@ -403,7 +393,6 @@ namespace {
formulasString += "; R=? [I=10]";
formulasString += "; R=? [C<=10]";
formulasString += "; R=? [F \"first_queue_full\"&\"second_queue_full\"]";
formulasString += "; LRA=? [\"first_queue_full\"]";
auto modelFormulas = this->buildModelFormulas(STORM_TEST_RESOURCES_DIR "/ctmc/tandem5.sm", formulasString);
auto model = std::move(modelFormulas.first);
@ -431,9 +420,6 @@ namespace {
result = checker->check(this->env(), tasks[5]);
EXPECT_NEAR(this->parseNumber("262.85103824276308"), this->getQuantitativeResultAtInitialState(model, result), this->precision());
result = checker->check(this->env(), tasks[6]);
EXPECT_NEAR(this->parseNumber("0.9100373532"), this->getQuantitativeResultAtInitialState(model, result), this->precision());
}
TYPED_TEST(CtmcCslModelCheckerTest, simple2) {

400
src/test/storm/modelchecker/csl/LraCtmcCslModelCheckerTest.cpp
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@ -0,0 +1,400 @@
#include "test/storm_gtest.h"
#include "test/storm_gtest.h"
#include "storm-config.h"
#include "storm/api/builder.h"
#include "storm-parsers/api/model_descriptions.h"
#include "storm/api/properties.h"
#include "storm-conv/api/storm-conv.h"
#include "storm-parsers/api/properties.h"
#include "storm-parsers/parser/FormulaParser.h"
#include "storm/logic/Formulas.h"
#include "storm/models/sparse/StandardRewardModel.h"
#include "storm/models/symbolic/StandardRewardModel.h"
#include "storm/models/sparse/Ctmc.h"
#include "storm/models/symbolic/Ctmc.h"
#include "storm/modelchecker/csl/SparseCtmcCslModelChecker.h"
#include "storm/modelchecker/csl/HybridCtmcCslModelChecker.h"
#include "storm/modelchecker/results/QuantitativeCheckResult.h"
#include "storm/modelchecker/results/ExplicitQualitativeCheckResult.h"
#include "storm/modelchecker/results/SymbolicQualitativeCheckResult.h"
#include "storm/modelchecker/results/QualitativeCheckResult.h"
#include "storm-parsers/parser/PrismParser.h"
#include "storm/storage/expressions/ExpressionManager.h"
#include "storm/settings/modules/CoreSettings.h"
#include "storm/environment/solver/NativeSolverEnvironment.h"
#include "storm/environment/solver/GmmxxSolverEnvironment.h"
#include "storm/environment/solver/EigenSolverEnvironment.h"
#include "storm/environment/solver/LongRunAverageSolverEnvironment.h"
namespace {
enum class CtmcEngine {PrismSparse, JaniSparse, JaniHybrid};
class GBSparseGmmxxGmresIluEnvironment {
public:
static const storm::dd::DdType ddType = storm::dd::DdType::Sylvan; // unused for sparse models
static const CtmcEngine engine = CtmcEngine::PrismSparse;
static const bool isExact = false;
typedef double ValueType;
typedef storm::models::sparse::Ctmc<ValueType> ModelType;
static storm::Environment createEnvironment() {
storm::Environment env;
env.solver().setLinearEquationSolverType(storm::solver::EquationSolverType::Gmmxx);
env.solver().gmmxx().setMethod(storm::solver::GmmxxLinearEquationSolverMethod::Gmres);
env.solver().gmmxx().setPreconditioner(storm::solver::GmmxxLinearEquationSolverPreconditioner::Ilu);
env.solver().lra().setDetLraMethod(storm::solver::LraMethod::GainBiasEquations);
return env;
}
};
class GBJaniSparseGmmxxGmresIluEnvironment {
public:
static const storm::dd::DdType ddType = storm::dd::DdType::Sylvan; // unused for sparse models
static const CtmcEngine engine = CtmcEngine::JaniSparse;
static const bool isExact = false;
typedef double ValueType;
typedef storm::models::sparse::Ctmc<ValueType> ModelType;
static storm::Environment createEnvironment() {
storm::Environment env;
env.solver().setLinearEquationSolverType(storm::solver::EquationSolverType::Gmmxx);
env.solver().gmmxx().setMethod(storm::solver::GmmxxLinearEquationSolverMethod::Gmres);
env.solver().gmmxx().setPreconditioner(storm::solver::GmmxxLinearEquationSolverPreconditioner::Ilu);
env.solver().lra().setDetLraMethod(storm::solver::LraMethod::GainBiasEquations);
return env;
}
};
class GBJaniHybridCuddGmmxxGmresEnvironment {
public:
static const storm::dd::DdType ddType = storm::dd::DdType::CUDD;
static const CtmcEngine engine = CtmcEngine::JaniHybrid;
static const bool isExact = false;
typedef double ValueType;
typedef storm::models::symbolic::Ctmc<ddType, ValueType> ModelType;
static storm::Environment createEnvironment() {
storm::Environment env;
env.solver().setLinearEquationSolverType(storm::solver::EquationSolverType::Gmmxx);
env.solver().gmmxx().setMethod(storm::solver::GmmxxLinearEquationSolverMethod::Gmres);
env.solver().lra().setDetLraMethod(storm::solver::LraMethod::GainBiasEquations);
env.solver().lra().setPrecision(storm::utility::convertNumber<storm::RationalNumber>(1e-8)); // Need to increase precision because eq sys yields incorrect results
return env;
}
};
class GBJaniHybridSylvanGmmxxGmresEnvironment {
public:
static const storm::dd::DdType ddType = storm::dd::DdType::Sylvan;
static const CtmcEngine engine = CtmcEngine::JaniHybrid;
static const bool isExact = false;
typedef double ValueType;
typedef storm::models::symbolic::Ctmc<ddType, ValueType> ModelType;
static storm::Environment createEnvironment() {
storm::Environment env;
env.solver().setLinearEquationSolverType(storm::solver::EquationSolverType::Gmmxx);
env.solver().gmmxx().setMethod(storm::solver::GmmxxLinearEquationSolverMethod::Gmres);
env.solver().lra().setDetLraMethod(storm::solver::LraMethod::GainBiasEquations);
env.solver().lra().setPrecision(storm::utility::convertNumber<storm::RationalNumber>(1e-8)); // Need to increase precision because eq sys yields incorrect results
return env;
}
};
class GBSparseEigenDGmresEnvironment {
public:
static const storm::dd::DdType ddType = storm::dd::DdType::Sylvan; // unused for sparse models
static const CtmcEngine engine = CtmcEngine::PrismSparse;
static const bool isExact = false;
typedef double ValueType;
typedef storm::models::sparse::Ctmc<ValueType> ModelType;
static storm::Environment createEnvironment() {
storm::Environment env;
env.solver().setLinearEquationSolverType(storm::solver::EquationSolverType::Eigen);
env.solver().eigen().setMethod(storm::solver::EigenLinearEquationSolverMethod::DGmres);
env.solver().eigen().setPreconditioner(storm::solver::EigenLinearEquationSolverPreconditioner::Ilu);
env.solver().lra().setDetLraMethod(storm::solver::LraMethod::GainBiasEquations);
return env;
}
};
class GBSparseEigenRationalLUEnvironment {
public:
static const storm::dd::DdType ddType = storm::dd::DdType::Sylvan; // unused for sparse models
static const CtmcEngine engine = CtmcEngine::PrismSparse;
static const bool isExact = false;
typedef double ValueType;
typedef storm::models::sparse::Ctmc<ValueType> ModelType;
static storm::Environment createEnvironment() {
storm::Environment env;
env.solver().setLinearEquationSolverType(storm::solver::EquationSolverType::Eigen);
env.solver().eigen().setMethod(storm::solver::EigenLinearEquationSolverMethod::SparseLU);
env.solver().lra().setDetLraMethod(storm::solver::LraMethod::GainBiasEquations);
return env;
}
};
class GBSparseNativeSorEnvironment {
public:
static const storm::dd::DdType ddType = storm::dd::DdType::Sylvan; // unused for sparse models
static const CtmcEngine engine = CtmcEngine::PrismSparse;
static const bool isExact = false;
typedef double ValueType;
typedef storm::models::sparse::Ctmc<ValueType> ModelType;
static storm::Environment createEnvironment() {
storm::Environment env;
env.solver().setLinearEquationSolverType(storm::solver::EquationSolverType::Native);
env.solver().native().setMethod(storm::solver::NativeLinearEquationSolverMethod::SOR);
env.solver().native().setSorOmega(storm::utility::convertNumber<storm::RationalNumber>(0.7)); // LRA computation fails for 0.9
env.solver().native().setPrecision(storm::utility::convertNumber<storm::RationalNumber>(1e-9));
env.solver().native().setRelativeTerminationCriterion(false);
env.solver().lra().setDetLraMethod(storm::solver::LraMethod::GainBiasEquations);
return env;
}
};
class DistrSparseGmmxxGmresIluEnvironment {
public:
static const storm::dd::DdType ddType = storm::dd::DdType::Sylvan; // unused for sparse models
static const CtmcEngine engine = CtmcEngine::PrismSparse;
static const bool isExact = false;
typedef double ValueType;
typedef storm::models::sparse::Ctmc<ValueType> ModelType;
static storm::Environment createEnvironment() {
storm::Environment env;
env.solver().setLinearEquationSolverType(storm::solver::EquationSolverType::Gmmxx);
env.solver().gmmxx().setMethod(storm::solver::GmmxxLinearEquationSolverMethod::Gmres);
env.solver().gmmxx().setPreconditioner(storm::solver::GmmxxLinearEquationSolverPreconditioner::Ilu);
env.solver().lra().setDetLraMethod(storm::solver::LraMethod::LraDistributionEquations);
env.solver().lra().setPrecision(storm::utility::convertNumber<storm::RationalNumber>(1e-8)); // Need to increase precision because eq sys yields incorrect results
return env;
}
};
class DistrSparseEigenDoubleLUEnvironment {
public:
static const storm::dd::DdType ddType = storm::dd::DdType::Sylvan; // unused for sparse models
static const CtmcEngine engine = CtmcEngine::PrismSparse;
static const bool isExact = false;
typedef double ValueType;
typedef storm::models::sparse::Ctmc<ValueType> ModelType;
static storm::Environment createEnvironment() {
storm::Environment env;
env.solver().setLinearEquationSolverType(storm::solver::EquationSolverType::Eigen);
env.solver().eigen().setMethod(storm::solver::EigenLinearEquationSolverMethod::DGmres);
env.solver().eigen().setPreconditioner(storm::solver::EigenLinearEquationSolverPreconditioner::Ilu);
env.solver().eigen().setPrecision(storm::utility::convertNumber<storm::RationalNumber>(1e-8));
env.solver().lra().setDetLraMethod(storm::solver::LraMethod::LraDistributionEquations);
return env;
}
};
class ValueIterationSparseEnvironment {
public:
static const storm::dd::DdType ddType = storm::dd::DdType::Sylvan; // unused for sparse models
static const CtmcEngine engine = CtmcEngine::PrismSparse;
static const bool isExact = false;
typedef double ValueType;
typedef storm::models::sparse::Ctmc<ValueType> ModelType;
static storm::Environment createEnvironment() {
storm::Environment env;
env.solver().lra().setDetLraMethod(storm::solver::LraMethod::ValueIteration);
return env;
}
};
template<typename TestType>
class LraCtmcCslModelCheckerTest : public ::testing::Test {
public:
typedef typename TestType::ValueType ValueType;
typedef typename storm::models::sparse::Ctmc<ValueType> SparseModelType;
typedef typename storm::models::symbolic::Ctmc<TestType::ddType, ValueType> SymbolicModelType;
LraCtmcCslModelCheckerTest() : _environment(TestType::createEnvironment()) {}
storm::Environment const& env() const { return _environment; }
ValueType parseNumber(std::string const& input) const { return storm::utility::convertNumber<ValueType>(input);}
ValueType precision() const { return TestType::isExact ? parseNumber("0") : parseNumber("1e-6");}
bool isSparseModel() const { return std::is_same<typename TestType::ModelType, SparseModelType>::value; }
bool isSymbolicModel() const { return std::is_same<typename TestType::ModelType, SymbolicModelType>::value; }
CtmcEngine getEngine() const { return TestType::engine; }
template <typename MT = typename TestType::ModelType>
typename std::enable_if<std::is_same<MT, SparseModelType>::value, std::pair<std::shared_ptr<MT>, std::vector<std::shared_ptr<storm::logic::Formula const>>>>::type
buildModelFormulas(std::string const& pathToPrismFile, std::string const& formulasAsString, std::string const& constantDefinitionString = "") const {
std::pair<std::shared_ptr<MT>, std::vector<std::shared_ptr<storm::logic::Formula const>>> result;
storm::prism::Program program = storm::api::parseProgram(pathToPrismFile, true);
program = storm::utility::prism::preprocess(program, constantDefinitionString);
if (TestType::engine == CtmcEngine::PrismSparse) {
result.second = storm::api::extractFormulasFromProperties(storm::api::parsePropertiesForPrismProgram(formulasAsString, program));
result.first = storm::api::buildSparseModel<ValueType>(program, result.second)->template as<MT>();
} else if (TestType::engine == CtmcEngine::JaniSparse) {
auto janiData = storm::api::convertPrismToJani(program, storm::api::parsePropertiesForPrismProgram(formulasAsString, program));
janiData.first.substituteFunctions();
result.second = storm::api::extractFormulasFromProperties(janiData.second);
result.first = storm::api::buildSparseModel<ValueType>(janiData.first, result.second)->template as<MT>();
}
return result;
}
template <typename MT = typename TestType::ModelType>
typename std::enable_if<std::is_same<MT, SymbolicModelType>::value, std::pair<std::shared_ptr<MT>, std::vector<std::shared_ptr<storm::logic::Formula const>>>>::type
buildModelFormulas(std::string const& pathToPrismFile, std::string const& formulasAsString, std::string const& constantDefinitionString = "") const {
std::pair<std::shared_ptr<MT>, std::vector<std::shared_ptr<storm::logic::Formula const>>> result;
storm::prism::Program program = storm::api::parseProgram(pathToPrismFile, true);
program = storm::utility::prism::preprocess(program, constantDefinitionString);
if (TestType::engine == CtmcEngine::JaniHybrid) {
auto janiData = storm::api::convertPrismToJani(program, storm::api::parsePropertiesForPrismProgram(formulasAsString, program));
janiData.first.substituteFunctions();
result.second = storm::api::extractFormulasFromProperties(janiData.second);
result.first = storm::api::buildSymbolicModel<TestType::ddType, ValueType>(janiData.first, result.second)->template as<MT>();
}
return result;
}
std::vector<storm::modelchecker::CheckTask<storm::logic::Formula, ValueType>> getTasks(std::vector<std::shared_ptr<storm::logic::Formula const>> const& formulas) const {
std::vector<storm::modelchecker::CheckTask<storm::logic::Formula, ValueType>> result;
for (auto const& f : formulas) {
result.emplace_back(*f);
}
return result;
}
template <typename MT = typename TestType::ModelType>
typename std::enable_if<std::is_same<MT, SparseModelType>::value, std::shared_ptr<storm::modelchecker::AbstractModelChecker<MT>>>::type
createModelChecker(std::shared_ptr<MT> const& model) const {
if (TestType::engine == CtmcEngine::PrismSparse || TestType::engine == CtmcEngine::JaniSparse) {
return std::make_shared<storm::modelchecker::SparseCtmcCslModelChecker<SparseModelType>>(*model);
}
return nullptr;
}
template <typename MT = typename TestType::ModelType>
typename std::enable_if<std::is_same<MT, SymbolicModelType>::value, std::shared_ptr<storm::modelchecker::AbstractModelChecker<MT>>>::type
createModelChecker(std::shared_ptr<MT> const& model) const {
if (TestType::engine == CtmcEngine::JaniHybrid) {
return std::make_shared<storm::modelchecker::HybridCtmcCslModelChecker<SymbolicModelType>>(*model);
}
return nullptr;
}
bool getQualitativeResultAtInitialState(std::shared_ptr<storm::models::Model<ValueType>> const& model, std::unique_ptr<storm::modelchecker::CheckResult>& result) {
auto filter = getInitialStateFilter(model);
result->filter(*filter);
return result->asQualitativeCheckResult().forallTrue();
}
ValueType getQuantitativeResultAtInitialState(std::shared_ptr<storm::models::Model<ValueType>> const& model, std::unique_ptr<storm::modelchecker::CheckResult>& result) {
auto filter = getInitialStateFilter(model);
result->filter(*filter);
return result->asQuantitativeCheckResult<ValueType>().getMin();
}
private:
storm::Environment _environment;
std::unique_ptr<storm::modelchecker::QualitativeCheckResult> getInitialStateFilter(std::shared_ptr<storm::models::Model<ValueType>> const& model) const {
if (isSparseModel()) {
return std::make_unique<storm::modelchecker::ExplicitQualitativeCheckResult>(model->template as<SparseModelType>()->getInitialStates());
} else {
return std::make_unique<storm::modelchecker::SymbolicQualitativeCheckResult<TestType::ddType>>(model->template as<SymbolicModelType>()->getReachableStates(), model->template as<SymbolicModelType>()->getInitialStates());
}
}
};
typedef ::testing::Types<
GBSparseGmmxxGmresIluEnvironment,
GBJaniSparseGmmxxGmresIluEnvironment,
GBJaniHybridCuddGmmxxGmresEnvironment,
GBJaniHybridSylvanGmmxxGmresEnvironment,
GBSparseEigenDGmresEnvironment,
GBSparseEigenRationalLUEnvironment,
GBSparseNativeSorEnvironment,
DistrSparseGmmxxGmresIluEnvironment,
DistrSparseEigenDoubleLUEnvironment,
ValueIterationSparseEnvironment
> TestingTypes;
TYPED_TEST_SUITE(LraCtmcCslModelCheckerTest, TestingTypes,);
TYPED_TEST(LraCtmcCslModelCheckerTest, Cluster) {
std::string formulasString = "LRA=? [\"minimum\"]";
auto modelFormulas = this->buildModelFormulas(STORM_TEST_RESOURCES_DIR "/ctmc/cluster2.sm", formulasString);
auto model = std::move(modelFormulas.first);
auto tasks = this->getTasks(modelFormulas.second);
EXPECT_EQ(276ul, model->getNumberOfStates());
EXPECT_EQ(1120ul, model->getNumberOfTransitions());
ASSERT_EQ(model->getType(), storm::models::ModelType::Ctmc);
auto checker = this->createModelChecker(model);
std::unique_ptr<storm::modelchecker::CheckResult> result;
result = checker->check(this->env(), tasks[0]);
EXPECT_NEAR(this->parseNumber("0.99999766034263426"), this->getQuantitativeResultAtInitialState(model, result), this->precision());
}
TYPED_TEST(LraCtmcCslModelCheckerTest, Embedded) {
std::string formulasString = "LRA=? [\"fail_sensors\"]";
auto modelFormulas = this->buildModelFormulas(STORM_TEST_RESOURCES_DIR "/ctmc/embedded2.sm", formulasString);
auto model = std::move(modelFormulas.first);
auto tasks = this->getTasks(modelFormulas.second);
EXPECT_EQ(3478ul, model->getNumberOfStates());
EXPECT_EQ(14639ul, model->getNumberOfTransitions());
ASSERT_EQ(model->getType(), storm::models::ModelType::Ctmc);
auto checker = this->createModelChecker(model);
std::unique_ptr<storm::modelchecker::CheckResult> result;
result = checker->check(this->env(), tasks[0]);
EXPECT_NEAR(this->parseNumber("0.93458866427696596"), this->getQuantitativeResultAtInitialState(model, result), this->precision());
}
TYPED_TEST(LraCtmcCslModelCheckerTest, Polling) {
std::string formulasString = "LRA=?[\"target\"]";
auto modelFormulas = this->buildModelFormulas(STORM_TEST_RESOURCES_DIR "/ctmc/polling2.sm", formulasString);
auto model = std::move(modelFormulas.first);
auto tasks = this->getTasks(modelFormulas.second);
EXPECT_EQ(12ul, model->getNumberOfStates());
EXPECT_EQ(22ul, model->getNumberOfTransitions());
ASSERT_EQ(model->getType(), storm::models::ModelType::Ctmc);
auto checker = this->createModelChecker(model);
std::unique_ptr<storm::modelchecker::CheckResult> result;
result = checker->check(this->env(), tasks[0]);
EXPECT_NEAR(this->parseNumber("0.20079750055570736"), this->getQuantitativeResultAtInitialState(model, result), this->precision());
}
TYPED_TEST(LraCtmcCslModelCheckerTest, Tandem) {
std::string formulasString = "LRA=? [\"first_queue_full\"]";
auto modelFormulas = this->buildModelFormulas(STORM_TEST_RESOURCES_DIR "/ctmc/tandem5.sm", formulasString);
auto model = std::move(modelFormulas.first);
auto tasks = this->getTasks(modelFormulas.second);
EXPECT_EQ(66ul, model->getNumberOfStates());
EXPECT_EQ(189ul, model->getNumberOfTransitions());
ASSERT_EQ(model->getType(), storm::models::ModelType::Ctmc);
auto checker = this->createModelChecker(model);
std::unique_ptr<storm::modelchecker::CheckResult> result;
result = checker->check(this->env(), tasks[0]);
EXPECT_NEAR(this->parseNumber("0.9100373532"), this->getQuantitativeResultAtInitialState(model, result), this->precision());
}
TYPED_TEST(LraCtmcCslModelCheckerTest, Rewards) {
std::string formulasString = "R=? [ LRA ]";
auto modelFormulas = this->buildModelFormulas(STORM_TEST_RESOURCES_DIR "/ctmc/lrarewards.sm", formulasString);
auto model = std::move(modelFormulas.first);
auto tasks = this->getTasks(modelFormulas.second);
EXPECT_EQ(4ul, model->getNumberOfStates());
EXPECT_EQ(6ul, model->getNumberOfTransitions());
ASSERT_EQ(model->getType(), storm::models::ModelType::Ctmc);
auto checker = this->createModelChecker(model);
std::unique_ptr<storm::modelchecker::CheckResult> result;
result = checker->check(this->env(), tasks[0]);
EXPECT_NEAR(this->parseNumber("11/15"), this->getQuantitativeResultAtInitialState(model, result), this->precision());
}
}
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