NondeterministicModel.cpp

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#include "storm/models/sparse/NondeterministicModel.h"
 
#include "storm/adapters/IntervalAdapter.h"
#include "storm/adapters/RationalFunctionAdapter.h"
#include "storm/adapters/RationalNumberAdapter.h"
#include "storm/io/export.h"
#include "storm/models/sparse/MarkovAutomaton.h"
#include "storm/models/sparse/StandardRewardModel.h"
#include "storm/storage/Scheduler.h"
#include "storm/storage/memorystructure/MemoryStructureBuilder.h"
#include "storm/storage/memorystructure/SparseModelMemoryProduct.h"
#include "storm/transformer/SubsystemBuilder.h"
 
namespace storm {
namespace models {
namespace sparse {
 
template<typename ValueType, typename RewardModelType>
NondeterministicModel<ValueType, RewardModelType>::NondeterministicModel(ModelType modelType,
                                                                         storm::storage::sparse::ModelComponents<ValueType, RewardModelType> const& components)
    : Model<ValueType, RewardModelType>(modelType, components) {
    // Intentionally left empty
}
 
template<typename ValueType, typename RewardModelType>
NondeterministicModel<ValueType, RewardModelType>::NondeterministicModel(ModelType modelType,
                                                                         storm::storage::sparse::ModelComponents<ValueType, RewardModelType>&& components)
    : Model<ValueType, RewardModelType>(modelType, std::move(components)) {
    // Intentionally left empty
}
 
template<typename ValueType, typename RewardModelType>
std::vector<uint_fast64_t> const& NondeterministicModel<ValueType, RewardModelType>::getNondeterministicChoiceIndices() const {
    return this->getTransitionMatrix().getRowGroupIndices();
}
 
template<typename ValueType, typename RewardModelType>
uint_fast64_t NondeterministicModel<ValueType, RewardModelType>::getNumberOfChoices(uint_fast64_t state) const {
    auto indices = this->getNondeterministicChoiceIndices();
    return indices[state + 1] - indices[state];
}
 
template<typename ValueType, typename RewardModelType>
void NondeterministicModel<ValueType, RewardModelType>::reduceToStateBasedRewards() {
    for (auto& rewardModel : this->getRewardModels()) {
        rewardModel.second.reduceToStateBasedRewards(this->getTransitionMatrix(), false);
    }
}
 
template<typename ValueType, typename RewardModelType>
std::shared_ptr<storm::models::sparse::Model<ValueType, RewardModelType>> NondeterministicModel<ValueType, RewardModelType>::applyScheduler(
    storm::storage::Scheduler<ValueType> const& scheduler, bool dropUnreachableStates, bool preserveModelType) const {
    if (scheduler.isMemorylessScheduler() && scheduler.isDeterministicScheduler() && !scheduler.isPartialScheduler()) {
        // Special case with improved handling.
        storm::storage::BitVector actionSelection = scheduler.computeActionSupport(getNondeterministicChoiceIndices());
        storm::storage::BitVector allStates(this->getNumberOfStates(), true);
        transformer::SubsystemBuilderOptions options;
        options.makeRowGroupingTrivial = !preserveModelType;
        auto res = storm::transformer::buildSubsystem(*this, allStates, actionSelection, !dropUnreachableStates, options);
        return res.model;
    } else {
        storm::storage::SparseModelMemoryProduct<ValueType, RewardModelType> memoryProduct(*this, scheduler);
        if (!dropUnreachableStates) {
            memoryProduct.setBuildFullProduct();
        }
        return memoryProduct.build(preserveModelType);
    }
}
 
template<typename ValueType, typename RewardModelType>
void NondeterministicModel<ValueType, RewardModelType>::printModelInformationToStream(std::ostream& out) const {
    this->printModelInformationHeaderToStream(out);
    out << "Choices: \t" << this->getNumberOfChoices() << '\n';
    this->printModelInformationFooterToStream(out);
}
 
template<typename ValueType, typename RewardModelType>
void NondeterministicModel<ValueType, RewardModelType>::writeDotToStream(std::ostream& outStream, size_t maxWidthLabel, bool includeLabeling,
                                                                         storm::storage::BitVector const* subsystem, std::vector<ValueType> const* firstValue,
                                                                         std::vector<ValueType> const* secondValue,
                                                                         std::vector<uint_fast64_t> const* stateColoring,
                                                                         std::vector<std::string> const* colors, std::vector<uint_fast64_t>* scheduler,
                                                                         bool finalizeOutput) const {
    Model<ValueType, RewardModelType>::writeDotToStream(outStream, maxWidthLabel, includeLabeling, subsystem, firstValue, secondValue, stateColoring, colors,
                                                        scheduler, false);
 
    // Write the probability distributions for all the states.
    for (uint_fast64_t state = 0; state < this->getNumberOfStates(); ++state) {
        uint_fast64_t rowCount = this->getNondeterministicChoiceIndices()[state + 1] - this->getNondeterministicChoiceIndices()[state];
        bool highlightChoice = true;
 
        // For this, we need to iterate over all available nondeterministic choices in the current state.
        for (uint_fast64_t choice = 0; choice < rowCount; ++choice) {
            uint_fast64_t rowIndex = this->getNondeterministicChoiceIndices()[state] + choice;
            typename storm::storage::SparseMatrix<ValueType>::const_rows row = this->getTransitionMatrix().getRow(rowIndex);
 
            if (scheduler != nullptr) {
                // If the scheduler picked the current choice, we will not make it dotted, but highlight it.
                if ((*scheduler)[state] == choice) {
                    highlightChoice = true;
                } else {
                    highlightChoice = false;
                }
            }
 
            // For each nondeterministic choice, we draw an arrow to an intermediate node to better display
            // the grouping of transitions.
 
            // The intermediate node:
            outStream << "\t\"" << state << "c" << choice << "\" [shape = \"point\"";
            // If we were given a scheduler to highlight, we do so now.
            if (scheduler != nullptr) {
                if (highlightChoice) {
                    outStream << ", fillcolor=\"red\"";
                }
            }
            outStream << "];\n";
 
            // The arrow to the intermediate node:
            outStream << "\t" << state << " -> \"" << state << "c" << choice << "\"";
            bool arrowHasLabel = false;
            if (this->isOfType(ModelType::MarkovAutomaton)) {
                // If this is a Markov automaton, we have to check whether the current choice is a Markovian choice and correspondingly print the exit rate
                MarkovAutomaton<ValueType, RewardModelType> const* ma = dynamic_cast<MarkovAutomaton<ValueType, RewardModelType> const*>(this);
                if (ma->isMarkovianState(state) && choice == 0) {
                    arrowHasLabel = true;
                    outStream << " [ label = \"" << ma->getExitRate(state);
                }
            }
            if (this->hasChoiceLabeling()) {
                if (arrowHasLabel) {
                    outStream << " | {";
                } else {
                    outStream << " [ label = \"{";
                }
                arrowHasLabel = true;
                storm::io::outputFixedWidth(outStream, this->getChoiceLabeling().getLabelsOfChoice(rowIndex), maxWidthLabel);
                outStream << "}";
            }
            if (arrowHasLabel) {
                outStream << "\"";
            }
            // If we were given a scheduler to highlight, we do so now.
            if (scheduler != nullptr) {
                if (arrowHasLabel) {
                    outStream << ", ";
                } else {
                    outStream << "[ ";
                }
                if (highlightChoice) {
                    outStream << "color=\"red\", penwidth = 2";
                } else {
                    outStream << "style = \"dotted\"";
                }
            }
 
            if (arrowHasLabel || scheduler != nullptr) {
                outStream << "]\n";
            }
            outStream << ";\n";
 
            // Now draw all probabilistic arcs that belong to this non-deterministic choice.
            for (auto const& transition : row) {
                if (subsystem == nullptr || subsystem->get(transition.getColumn())) {
                    outStream << "\t\"" << state << "c" << choice << "\" -> " << transition.getColumn() << " [ label= \"" << transition.getValue() << "\" ]";
 
                    // If we were given a scheduler to highlight, we do so now.
                    if (scheduler != nullptr) {
                        if (highlightChoice) {
                            outStream << " [color=\"red\", penwidth = 2]";
                        } else {
                            outStream << " [style = \"dotted\"]";
                        }
                    }
                    outStream << ";\n";
                }
            }
        }
    }
 
    if (finalizeOutput) {
        outStream << "}\n";
    }
}
 
template<typename ValueType, typename RewardModelType>
uint_least64_t NondeterministicModel<ValueType, RewardModelType>::getChoiceIndex(storm::storage::StateActionPair const& stateactPair) const {
    return this->getNondeterministicChoiceIndices()[stateactPair.getState()] + stateactPair.getAction();
}
 
template class NondeterministicModel<double>;
template class NondeterministicModel<double, storm::models::sparse::StandardRewardModel<storm::Interval>>;
template class NondeterministicModel<storm::Interval>;
template class NondeterministicModel<storm::RationalNumber>;
template class NondeterministicModel<storm::RationalFunction>;
}  // namespace sparse
}  // namespace models
}  // namespace storm