def shortest_path(graph, sourceVertex):
min_heap = PriorityQueue(True)
distance = {}
parent = {}
for vertex in graph.all_vertex.values():
min_heap.add_task(sys.maxsize, vertex)
min_heap.change_task_priority(0, sourceVertex)
distance[sourceVertex] = 0
parent[sourceVertex] = None
while min_heap.is_empty() is False:
task = min_heap.peek_task()
weight = min_heap.get_task_priority(task)
current = min_heap.pop_task()
distance[current] = weight
for edge in current.edges:
adjacent = get_other_vertex_for_edge(current, edge)
if min_heap.contains_task(adjacent) is False:
continue
new_distance = distance[current] + edge.weight;
if min_heap.get_task_priority(adjacent) > new_distance:
min_heap.change_task_priority(new_distance, adjacent)
parent[adjacent] = current
return distance
def minimum_spanning_tree(graph):
min_heap = PriorityQueue(True)
vertex_to_edge = {}
result = []
for vertex in graph.all_vertex.values():
min_heap.add_task(sys.maxsize, vertex)
start_vertex = next(iter((graph.all_vertex.values())))
min_heap.change_task_priority(0, start_vertex)
while min_heap.is_empty() is False:
current = min_heap.pop_task()
if (current in vertex_to_edge):
spanning_tree_edge = vertex_to_edge[current]
result.append(spanning_tree_edge)
for edge in current.edges:
adjacent = get_other_vertex_for_edge(current, edge)
if min_heap.contains_task(
adjacent) is True and min_heap.get_task_priority(
adjacent) > edge.weight:
min_heap.change_task_priority(edge.weight, adjacent)
vertex_to_edge[adjacent] = edge
return result