@ -35,7 +35,7 @@ namespace storm {
STORM_LOG_THROW ( parametricModel . hasUniqueRewardModel ( ) , storm : : exceptions : : InvalidArgumentException , " The rewardmodel of the approximation model should be unique " ) ;
STORM_LOG_THROW ( parametricModel . getUniqueRewardModel ( ) - > second . hasOnlyStateRewards ( ) , storm : : exceptions : : InvalidArgumentException , " The rewardmodel of the approximation model should have state rewards only " ) ;
} else {
STORM_LOG_THROW ( false , storm : : exceptions : : InvalidArgumentException , " Invalid formula: " < < this - > formula < < " . Approximation model only supports eventually or reachability reward formulae. " ) ;
STORM_LOG_THROW ( false , storm : : exceptions : : InvalidArgumentException , " Invalid formula: " < < formula < < " . Approximation model only supports eventually or reachability reward formulae. " ) ;
}
STORM_LOG_THROW ( parametricModel . hasLabel ( " target " ) , storm : : exceptions : : InvalidArgumentException , " The given Model has no \" target \" -statelabel. " ) ;
this - > targetStates = parametricModel . getStateLabeling ( ) . getStates ( " target " ) ;
@ -63,7 +63,7 @@ namespace storm {
this - > matrixData . assignment . shrink_to_fit ( ) ;
this - > vectorData . assignment . shrink_to_fit ( ) ;
if ( parametricModel . getType ( ) = = storm : : models : : ModelType : : Mdp ) {
initializePlayer1Matrix ( parametricModel , newIndices ) ;
initializePlayer1Matrix ( parametricModel ) ;
}
this - > eqSysResult = std : : vector < ConstantType > ( maybeStates . getNumberOfSetBits ( ) , this - > computeRewards ? storm : : utility : : one < ConstantType > ( ) : ConstantType ( 0.5 ) ) ;
@ -86,7 +86,7 @@ namespace storm {
false , // no force dimensions
true , //will have custom row grouping
numOfMaybeStates ) ; //exact number of rowgroups is unknown at this point, but at least this many
rowSubstitutions . push_back ( numOfMaybeStates ) ;
rowSubstitutions . reserve ( numOfMaybeStates ) ;
std : : size_t curRow = 0 ;
for ( auto oldRowGroup : this - > maybeStates ) {
for ( std : : size_t oldRow = parametricModel . getTransitionMatrix ( ) . getRowGroupIndices ( ) [ oldRowGroup ] ; oldRow < parametricModel . getTransitionMatrix ( ) . getRowGroupIndices ( ) [ oldRowGroup + 1 ] ; + + oldRow ) {
@ -123,8 +123,8 @@ namespace storm {
matrixBuilder . addNextValue ( curRow , newIndices [ oldEntry . getColumn ( ) ] , dummyValue ) ;
}
}
+ + curRow ;
}
+ + curRow ;
}
}
this - > matrixData . matrix = matrixBuilder . build ( ) ;
@ -198,7 +198,7 @@ namespace storm {
if ( storm : : utility : : isConstant ( parametricModel . getUniqueRewardModel ( ) - > second . getStateRewardVector ( ) [ oldState ] ) ) {
ConstantType reward = storm : : utility : : region : : convertNumber < ConstantType > ( storm : : utility : : region : : getConstantPart ( parametricModel . getUniqueRewardModel ( ) - > second . getStateRewardVector ( ) [ oldState ] ) ) ;
//Add one of these entries for every row in the row group of oldState
for ( auto matrixRow = this - > matrixData . matrix . getRowGroupIndices ( ) [ oldState ] ; matrixRow < this - > matrixData . matrix . getRowGroupIndices ( ) [ s tate+ 1 ] ; + + matrixRow ) {
for ( auto matrixRow = this - > matrixData . matrix . getRowGroupIndices ( ) [ oldState ] ; matrixRow < this - > matrixData . matrix . getRowGroupIndices ( ) [ oldS tate+ 1 ] ; + + matrixRow ) {
* vectorIt = reward ;
+ + vectorIt ;
}
@ -209,7 +209,7 @@ namespace storm {
// We might find out that the reward is independent of the probability parameters (and will thus be independent of nondeterministic choices)
// In that case, the reward function and the substitution will not change and thus we can use the same FunctionSubstitution
bool rewardDependsOnProbVars = true ;
std : : unordered_map < FunctionSubstitution , ConstantType , FuncSubHash > : : iterator functionsIt ;
typename std : : unordered_map < FunctionSubstitution , ConstantType , FuncSubHash > : : iterator functionsIt ;
for ( auto matrixRow = this - > matrixData . matrix . getRowGroupIndices ( ) [ oldState ] ; matrixRow < this - > matrixData . matrix . getRowGroupIndices ( ) [ oldState + 1 ] ; + + matrixRow ) {
auto probabilitySub = this - > funcSubData . substitutions [ rowSubstitutions [ matrixRow ] ] ;
if ( rewardDependsOnProbVars ) { //always executed in first iteration
@ -227,7 +227,7 @@ namespace storm {
}
// insert the substitution and the FunctionSubstitution
std : : size_t substitutionIndex = storm : : utility : : vector : : findOrInsert ( this - > funcSubData . substitutions , std : : move ( substitution ) ) ;
functionsIt = this - > funcSubData . functions . insert ( FunctionEntry ( FunctionSubstitution ( parametricModel . getUniqueRewardModel ( ) - > second . getStateRewardVector ( ) [ s tate] , substitutionIndex ) , dummyValue ) ) . first ;
functionsIt = this - > funcSubData . functions . insert ( FunctionEntry ( FunctionSubstitution ( parametricModel . getUniqueRewardModel ( ) - > second . getStateRewardVector ( ) [ oldS tate] , substitutionIndex ) , dummyValue ) ) . first ;
}
//insert assignment and dummy data
this - > vectorData . assignment . emplace_back ( std : : make_pair ( vectorIt , & ( functionsIt - > second ) ) ) ;
@ -239,7 +239,25 @@ namespace storm {
STORM_LOG_THROW ( vectorIt = = this - > vectorData . vector . end ( ) , storm : : exceptions : : UnexpectedException , " initRewards: The size of the eq-sys vector is not as it was expected " ) ;
}
template < typename ParametricSparseModelType , typename ConstantType >
void ApproximationModel < ParametricSparseModelType , ConstantType > : : initializePlayer1Matrix ( ParametricSparseModelType const & parametricModel ) {
std : : size_t p1MatrixSize = matrixData . matrix . getRowGroupCount ( ) ;
storm : : storage : : SparseMatrixBuilder < storm : : storage : : sparse : : state_type > matrixBuilder ( p1MatrixSize , //rows
p1MatrixSize , //columns
p1MatrixSize , //entries
true , // force dimensions
true , //will have custom row grouping
this - > maybeStates . getNumberOfSetBits ( ) ) ; // number of rowgroups
std : : size_t curRow = 0 ;
for ( auto oldRowGroup : this - > maybeStates ) {
matrixBuilder . newRowGroup ( curRow ) ;
for ( std : : size_t oldRow = parametricModel . getTransitionMatrix ( ) . getRowGroupIndices ( ) [ oldRowGroup ] ; oldRow < parametricModel . getTransitionMatrix ( ) . getRowGroupIndices ( ) [ oldRowGroup + 1 ] ; + + oldRow ) {
matrixBuilder . addNextValue ( curRow , curRow , storm : : utility : : one < storm : : storage : : sparse : : state_type > ( ) ) ;
+ + curRow ;
}
}
this - > player1Matrix = matrixBuilder . build ( ) ;
}
template < typename ParametricSparseModelType , typename ConstantType >
ApproximationModel < ParametricSparseModelType , ConstantType > : : ~ ApproximationModel ( ) {
@ -263,6 +281,7 @@ namespace storm {
ConstantType ApproximationModel < ParametricSparseModelType , ConstantType > : : computeInitialStateValue ( ParameterRegion < ParametricType > const & region , bool computeLowerBounds ) {
instantiate ( region , computeLowerBounds ) ;
invokeSolver ( computeLowerBounds ) ;
// std::cout << "initialStateValue is " << this->eqSysResult[this->eqSysInitIndex] << std::endl;
return this - > eqSysResult [ this - > eqSysInitIndex ] ;
}
@ -297,7 +316,7 @@ namespace storm {
auto & result = functionResult . second ;
result = computeLowerBounds ? storm : : utility : : infinity < ConstantType > ( ) : storm : : utility : : zero < ConstantType > ( ) ;
//Iterate over the different combinations of lower and upper bounds and update the min and max values
auto const & vertices = region . getVerticesOfRegion ( this - > choseOptimalParameters [ funcSub . getSubstitution ( ) ] ) ;
auto const & vertices = region . getVerticesOfRegion ( choseOptimalParameters [ funcSub . getSubstitution ( ) ] ) ;
for ( auto const & vertex : vertices ) {
//extend the substitution
for ( auto const & vertexSub : vertex ) {
TimQu
9 years ago