Use for M*n (m*N) tables queries forward (backward) MLD search
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@ -6,6 +6,7 @@ Feature: Basic Distance Matrix
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Background:
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Given the profile "testbot"
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And the partition extra arguments "--small-component-size 1 --max-cell-sizes 2,4,8,16"
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Scenario: Testbot - Travel time matrix of minimal network
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Given the node map
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@ -124,7 +125,7 @@ Feature: Basic Distance Matrix
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| d | 20 | 30 | 0 | 30 |
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| e | 30 | 40 | 10 | 0 |
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Scenario: Testbot - Travel time matrix and with only one source
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Scenario: Testbot - Rectangular travel time matrix
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Given the node map
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"""
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a b c
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@ -150,6 +151,39 @@ Feature: Basic Distance Matrix
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| e | 20 |
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| f | 30 |
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When I request a travel time matrix I should get
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| | a | b | e | f |
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| a | 0 | 10 | 20 | 30 |
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| b | 10 | 0 | 10 | 20 |
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When I request a travel time matrix I should get
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| | a | b |
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| a | 0 | 10 |
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| b | 10 | 0 |
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| e | 20 | 10 |
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| f | 30 | 20 |
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When I request a travel time matrix I should get
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| | a | b | e | f |
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| a | 0 | 10 | 20 | 30 |
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| b | 10 | 0 | 10 | 20 |
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| e | 20 | 10 | 0 | 10 |
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When I request a travel time matrix I should get
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| | a | b | e |
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| a | 0 | 10 | 20 |
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| b | 10 | 0 | 10 |
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| e | 20 | 10 | 0 |
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| f | 30 | 20 | 10 |
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When I request a travel time matrix I should get
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| | a | b | e | f |
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| a | 0 | 10 | 20 | 30 |
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| b | 10 | 0 | 10 | 20 |
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| e | 20 | 10 | 0 | 10 |
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| f | 30 | 20 | 10 | 0 |
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Scenario: Testbot - Travel time 3x2 matrix
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Given the node map
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"""
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@ -19,7 +19,7 @@ namespace routing_algorithms
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namespace mld
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{
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template <bool DIRECTION, typename MultiLevelPartition>
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template <typename MultiLevelPartition>
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inline LevelID getNodeQueryLevel(const MultiLevelPartition &partition,
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const NodeID node,
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const PhantomNode &phantom_node)
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@ -33,13 +33,24 @@ inline LevelID getNodeQueryLevel(const MultiLevelPartition &partition,
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const auto node_level = std::min(highest_diffrent_level(phantom_node.forward_segment_id),
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highest_diffrent_level(phantom_node.reverse_segment_id));
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if (DIRECTION == REVERSE_DIRECTION && node_level >= partition.GetNumberOfLevels() - 1)
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return node_level;
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}
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template <typename MultiLevelPartition>
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inline LevelID getNodeQueryLevel(const MultiLevelPartition &partition,
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const NodeID node,
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const PhantomNode &phantom_node,
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const LevelID maximal_level)
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{
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const auto node_level = getNodeQueryLevel(partition, node, phantom_node);
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if (node_level >= maximal_level)
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return INVALID_LEVEL_ID;
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return node_level;
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}
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template <bool DIRECTION, typename MultiLevelPartition>
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template <typename MultiLevelPartition>
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inline LevelID getNodeQueryLevel(const MultiLevelPartition &partition,
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NodeID node,
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const std::vector<PhantomNode> &phantom_nodes,
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@ -83,7 +94,7 @@ void relaxOutgoingEdges(const DataFacade<mld::Algorithm> &facade,
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const auto &partition = facade.GetMultiLevelPartition();
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const auto level = getNodeQueryLevel<DIRECTION>(partition, node, args...);
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const auto level = getNodeQueryLevel(partition, node, args...);
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// Break outgoing edges relaxation if node at the restricted level
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if (level == INVALID_LEVEL_ID)
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@ -192,68 +203,9 @@ void relaxOutgoingEdges(const DataFacade<mld::Algorithm> &facade,
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}
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}
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void forwardRoutingStep(const DataFacade<Algorithm> &facade,
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const unsigned row_idx,
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const unsigned number_of_targets,
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typename SearchEngineData<Algorithm>::ManyToManyQueryHeap &query_heap,
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const std::vector<NodeBucket> &search_space_with_buckets,
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std::vector<EdgeWeight> &weights_table,
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std::vector<EdgeDuration> &durations_table,
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const PhantomNode &phantom_node)
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{
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const auto node = query_heap.DeleteMin();
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const auto source_weight = query_heap.GetKey(node);
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const auto source_duration = query_heap.GetData(node).duration;
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// Check if each encountered node has an entry
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const auto &bucket_list = std::equal_range(search_space_with_buckets.begin(),
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search_space_with_buckets.end(),
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node,
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NodeBucket::Compare());
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for (const auto ¤t_bucket : boost::make_iterator_range(bucket_list))
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{
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// Get target id from bucket entry
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const auto column_idx = current_bucket.column_index;
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const auto target_weight = current_bucket.weight;
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const auto target_duration = current_bucket.duration;
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auto ¤t_weight = weights_table[row_idx * number_of_targets + column_idx];
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auto ¤t_duration = durations_table[row_idx * number_of_targets + column_idx];
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// Check if new weight is better
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auto new_weight = source_weight + target_weight;
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auto new_duration = source_duration + target_duration;
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if (new_weight >= 0 &&
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std::tie(new_weight, new_duration) < std::tie(current_weight, current_duration))
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{
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current_weight = new_weight;
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current_duration = new_duration;
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}
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}
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relaxOutgoingEdges<FORWARD_DIRECTION>(
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facade, node, source_weight, source_duration, query_heap, phantom_node);
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}
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void backwardRoutingStep(const DataFacade<Algorithm> &facade,
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const unsigned column_idx,
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typename SearchEngineData<Algorithm>::ManyToManyQueryHeap &query_heap,
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std::vector<NodeBucket> &search_space_with_buckets,
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const PhantomNode &phantom_node)
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{
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const auto node = query_heap.DeleteMin();
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const auto target_weight = query_heap.GetKey(node);
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const auto target_duration = query_heap.GetData(node).duration;
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// Store settled nodes in search space bucket
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search_space_with_buckets.emplace_back(node, column_idx, target_weight, target_duration);
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relaxOutgoingEdges<REVERSE_DIRECTION>(
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facade, node, target_weight, target_duration, query_heap, phantom_node);
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}
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//
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// Unidirectional multi-layer Dijkstra search for 1-to-N and N-to-1 matrices
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//
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template <bool DIRECTION>
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std::vector<EdgeDuration> oneToManySearch(SearchEngineData<Algorithm> &engine_working_data,
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const DataFacade<Algorithm> &facade,
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@ -414,6 +366,83 @@ std::vector<EdgeDuration> oneToManySearch(SearchEngineData<Algorithm> &engine_wo
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return durations;
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}
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//
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// Bidirectional multi-layer Dijkstra search for M-to-N matrices
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//
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template <bool DIRECTION>
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void forwardRoutingStep(const DataFacade<Algorithm> &facade,
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const unsigned row_idx,
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const unsigned number_of_sources,
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const unsigned number_of_targets,
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typename SearchEngineData<Algorithm>::ManyToManyQueryHeap &query_heap,
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const std::vector<NodeBucket> &search_space_with_buckets,
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std::vector<EdgeWeight> &weights_table,
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std::vector<EdgeDuration> &durations_table,
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const PhantomNode &phantom_node)
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{
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const auto node = query_heap.DeleteMin();
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const auto source_weight = query_heap.GetKey(node);
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const auto source_duration = query_heap.GetData(node).duration;
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// Check if each encountered node has an entry
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const auto &bucket_list = std::equal_range(search_space_with_buckets.begin(),
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search_space_with_buckets.end(),
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node,
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NodeBucket::Compare());
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for (const auto ¤t_bucket : boost::make_iterator_range(bucket_list))
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{
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// Get target id from bucket entry
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const auto column_idx = current_bucket.column_index;
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const auto target_weight = current_bucket.weight;
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const auto target_duration = current_bucket.duration;
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// Get the value location in the results tables:
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// * row-major direct (row_idx, column_idx) index for forward direction
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// * row-major transposed (column_idx, row_idx) for reversed direction
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const auto location = DIRECTION == FORWARD_DIRECTION
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? row_idx * number_of_targets + column_idx
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: row_idx + column_idx * number_of_sources;
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auto ¤t_weight = weights_table[location];
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auto ¤t_duration = durations_table[location];
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// Check if new weight is better
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auto new_weight = source_weight + target_weight;
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auto new_duration = source_duration + target_duration;
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if (new_weight >= 0 &&
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std::tie(new_weight, new_duration) < std::tie(current_weight, current_duration))
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{
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current_weight = new_weight;
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current_duration = new_duration;
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}
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}
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relaxOutgoingEdges<DIRECTION>(
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facade, node, source_weight, source_duration, query_heap, phantom_node);
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}
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template <bool DIRECTION>
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void backwardRoutingStep(const DataFacade<Algorithm> &facade,
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const unsigned column_idx,
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typename SearchEngineData<Algorithm>::ManyToManyQueryHeap &query_heap,
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std::vector<NodeBucket> &search_space_with_buckets,
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const PhantomNode &phantom_node)
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{
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const auto node = query_heap.DeleteMin();
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const auto target_weight = query_heap.GetKey(node);
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const auto target_duration = query_heap.GetData(node).duration;
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// Store settled nodes in search space bucket
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search_space_with_buckets.emplace_back(node, column_idx, target_weight, target_duration);
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const auto &partition = facade.GetMultiLevelPartition();
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const auto maximal_level = partition.GetNumberOfLevels() - 1;
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relaxOutgoingEdges<!DIRECTION>(
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facade, node, target_weight, target_duration, query_heap, phantom_node, maximal_level);
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}
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template <bool DIRECTION>
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std::vector<EdgeDuration> manyToManySearch(SearchEngineData<Algorithm> &engine_working_data,
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const DataFacade<Algorithm> &facade,
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const std::vector<PhantomNode> &phantom_nodes,
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@ -438,12 +467,17 @@ std::vector<EdgeDuration> manyToManySearch(SearchEngineData<Algorithm> &engine_w
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engine_working_data.InitializeOrClearManyToManyThreadLocalStorage(
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facade.GetNumberOfNodes());
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auto &query_heap = *(engine_working_data.many_to_many_heap);
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insertTargetInHeap(query_heap, phantom);
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if (DIRECTION == FORWARD_DIRECTION)
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insertTargetInHeap(query_heap, phantom);
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else
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insertSourceInHeap(query_heap, phantom);
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// explore search space
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while (!query_heap.Empty())
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{
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backwardRoutingStep(facade, column_idx, query_heap, search_space_with_buckets, phantom);
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backwardRoutingStep<DIRECTION>(
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facade, column_idx, query_heap, search_space_with_buckets, phantom);
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}
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}
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@ -460,19 +494,24 @@ std::vector<EdgeDuration> manyToManySearch(SearchEngineData<Algorithm> &engine_w
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engine_working_data.InitializeOrClearManyToManyThreadLocalStorage(
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facade.GetNumberOfNodes());
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auto &query_heap = *(engine_working_data.many_to_many_heap);
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insertSourceInHeap(query_heap, phantom);
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if (DIRECTION == FORWARD_DIRECTION)
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insertSourceInHeap(query_heap, phantom);
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else
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insertTargetInHeap(query_heap, phantom);
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// Explore search space
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while (!query_heap.Empty())
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{
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forwardRoutingStep(facade,
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row_idx,
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number_of_targets,
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query_heap,
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search_space_with_buckets,
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weights_table,
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durations_table,
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phantom);
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forwardRoutingStep<DIRECTION>(facade,
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row_idx,
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number_of_sources,
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number_of_targets,
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query_heap,
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search_space_with_buckets,
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weights_table,
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durations_table,
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phantom);
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}
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}
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@ -489,6 +528,10 @@ std::vector<EdgeDuration> manyToManySearch(SearchEngineData<Algorithm> &engine_w
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//
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// * many-to-many search tasks use a bidirectional Dijkstra search
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// with the candidate node level `min(GetHighestDifferentLevel(phantom_node, node))`
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// Due to pruned backward search space it is always better to compute the durations matrix
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// when number of sources is less than targets. If number of targets is less than sources
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// then search is performed on a reversed graph with phantom nodes with flipped roles and
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// returning a transposed matrix.
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template <>
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std::vector<EdgeDuration> manyToManySearch(SearchEngineData<mld::Algorithm> &engine_working_data,
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const DataFacade<mld::Algorithm> &facade,
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@ -508,7 +551,13 @@ std::vector<EdgeDuration> manyToManySearch(SearchEngineData<mld::Algorithm> &eng
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engine_working_data, facade, phantom_nodes, target_indices.front(), source_indices);
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}
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return mld::manyToManySearch(
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if (target_indices.size() < source_indices.size())
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{
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return mld::manyToManySearch<REVERSE_DIRECTION>(
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engine_working_data, facade, phantom_nodes, target_indices, source_indices);
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}
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return mld::manyToManySearch<FORWARD_DIRECTION>(
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engine_working_data, facade, phantom_nodes, source_indices, target_indices);
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}
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