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@ -13,15 +13,12 @@ namespace storm { |
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namespace helper { |
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namespace helper { |
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template<typename ValueType> |
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template<typename ValueType> |
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BaierUpperRewardBoundsComputer<ValueType>::BaierUpperRewardBoundsComputer(storm::storage::SparseMatrix<ValueType> const& transitionMatrix, std::vector<ValueType> const& rewards, std::vector<ValueType> const& oneStepTargetProbabilities) : transitionMatrix(transitionMatrix), rewards(rewards), oneStepTargetProbabilities(oneStepTargetProbabilities) { |
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BaierUpperRewardBoundsComputer<ValueType>::BaierUpperRewardBoundsComputer(storm::storage::SparseMatrix<ValueType> const& transitionMatrix, std::vector<ValueType> const& rewards, std::vector<ValueType> const& oneStepTargetProbabilities) : _transitionMatrix(transitionMatrix), _rewards(rewards), _oneStepTargetProbabilities(oneStepTargetProbabilities) { |
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// Intentionally left empty.
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// Intentionally left empty.
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} |
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} |
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template<typename ValueType> |
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template<typename ValueType> |
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ValueType BaierUpperRewardBoundsComputer<ValueType>::computeUpperBound() { |
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STORM_LOG_TRACE("Computing upper reward bounds using variant-2 of Baier et al."); |
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std::chrono::high_resolution_clock::time_point start = std::chrono::high_resolution_clock::now(); |
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std::vector<ValueType> BaierUpperRewardBoundsComputer<ValueType>::computeUpperBoundOnExpectedVisitingTimes(storm::storage::SparseMatrix<ValueType> const& transitionMatrix, std::vector<ValueType> const& oneStepTargetProbabilities) { |
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std::vector<uint64_t> stateToScc(transitionMatrix.getRowGroupCount()); |
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std::vector<uint64_t> stateToScc(transitionMatrix.getRowGroupCount()); |
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{ |
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{ |
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// Start with an SCC decomposition of the system.
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// Start with an SCC decomposition of the system.
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@ -106,18 +103,28 @@ namespace storm { |
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remainingStates.set(newStates.begin(), newStates.end(), false); |
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remainingStates.set(newStates.begin(), newStates.end(), false); |
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newStates.clear(); |
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newStates.clear(); |
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} |
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} |
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for (uint64_t state = 0; state < result.size(); ++state) { |
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ValueType maxReward = storm::utility::zero<ValueType>(); |
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for (auto row = transitionMatrix.getRowGroupIndices()[state], endRow = transitionMatrix.getRowGroupIndices()[state + 1]; row < endRow; ++row) { |
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maxReward = std::max(maxReward, rewards[row]); |
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} |
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result[state] = storm::utility::one<ValueType>() / result[state] * maxReward; |
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// Transform the d_t to an upper bound for zeta(t)
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for (auto& r : result) { |
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r = storm::utility::one<ValueType>() / r; |
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} |
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} |
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return result; |
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} |
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template<typename ValueType> |
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ValueType BaierUpperRewardBoundsComputer<ValueType>::computeUpperBound() { |
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STORM_LOG_TRACE("Computing upper reward bounds using variant-2 of Baier et al."); |
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std::chrono::high_resolution_clock::time_point start = std::chrono::high_resolution_clock::now(); |
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auto expVisits = computeUpperBoundOnExpectedVisitingTimes(_transitionMatrix, _oneStepTargetProbabilities); |
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ValueType upperBound = storm::utility::zero<ValueType>(); |
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ValueType upperBound = storm::utility::zero<ValueType>(); |
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for (auto const& e : result) { |
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upperBound += e; |
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for (uint64_t state = 0; state < expVisits.size(); ++state) { |
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ValueType maxReward = storm::utility::zero<ValueType>(); |
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for (auto row = _transitionMatrix.getRowGroupIndices()[state], endRow = _transitionMatrix.getRowGroupIndices()[state + 1]; row < endRow; ++row) { |
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maxReward = std::max(maxReward, _rewards[row]); |
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} |
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upperBound += expVisits[state] * maxReward; |
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} |
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} |
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STORM_LOG_TRACE("Baier algorithm for reward bound computation (variant 2) computed bound " << upperBound << "."); |
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STORM_LOG_TRACE("Baier algorithm for reward bound computation (variant 2) computed bound " << upperBound << "."); |
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