def rebalance_heaps(heap_low, heap_high):
rebalanced_heap_low = heap_low
rebalanced_heap_high = heap_high
if len(rebalanced_heap_low) - len(rebalanced_heap_high) > 1:
element = my_max_heap.heappop(rebalanced_heap_low)
my_min_heap.heappush(rebalanced_heap_high, element)
if len(rebalanced_heap_high) - len(rebalanced_heap_low) > 1:
element = my_min_heap.heappop(rebalanced_heap_high)
my_max_heap.heappush(rebalanced_heap_low, element)
return rebalanced_heap_low, rebalanced_heap_high
def dijkstra_heapbased(source_node, graph_adjacency_list):
"""
>>> dijkstra_heapbased(1, {1: [(2, 1), (3, 4)], 2: [(3, 2), (4, 6)], 3: [(1, 4), (2, 2), (4, 3)], 4: [(2, 6), (3, 3)]})
{1: 0, 2: 1, 3: 3, 4: 6}
>>> dijkstra_heapbased(1, {1: [(2, 1), (3, 4), (5, 0.5)], 2: [(3, 2), (4, 6)], 3: [(1, 4), (2, 2), (4, 3)], 4: [(2, 6), (3, 3)], 5: [(1, 0.5)]})
{1: 0, 2: 1, 3: 3, 4: 6, 5: 0.5}
:param source_node:
:param graph_adjacency_list:
:return:
"""
heap = []
shortest_distances = {}
explored = []
for node in graph_adjacency_list.keys():
if node == source_node:
my_min_heap.heappush(heap, (0, node))
else:
my_min_heap.heappush(heap, (float('inf'), node))
while len(heap):
current_distance, current_node = my_min_heap.heappop(heap)
shortest_distances[current_node] = current_distance
explored.append(current_node)
# udpate heap
adjacent_nodes = graph_adjacency_list.get(current_node, [])
unexplored_nodes = list(filter(lambda adjacent_node: not operator.contains(explored, adjacent_node[0]), adjacent_nodes))
for adjacent_node, adjacent_node_dist in unexplored_nodes:
possible_dist = current_distance + adjacent_node_dist
node_idx_in_heap = get_node_idx(heap, adjacent_node)
# do we need to update this particular node?
if node_idx_in_heap >= 0:
node_dist = heap[node_idx_in_heap][0]
if possible_dist < node_dist:
my_min_heap.remove(heap, node_idx_in_heap)
else:
continue
my_min_heap.heappush(heap, (possible_dist, adjacent_node))
return shortest_distances