def bellman_ford(g, w, s):
graph.init_single_source(g, s)
for i in range(1, len(g.vertices)):
for u,v in g.edges:
graph.relax(g.vertices[u], g.vertices[v], w)
for u,v in g.edges:
if g.vertices[v].d > g.vertices[u].d + w[(u,v)]:
return False
return True
def dijkstra(graph, weights, start_vertex):
graph_utils.init_single_source(graph, start_vertex)
queue = [vertex for vertex in graph.vertices.values()] # vertices to process
heapq.heapify(queue)
s = [] # set of vertices with shortest path already determined
while queue:
u = heapq.heappop(queue) # extract min
s.append(u)
for v in u.edges:
graph_utils.relax(u, v, weights)
def dijkstra(graph, weights, start_vertex):
graph_utils.init_single_source(graph, start_vertex)
queue = [vertex
for vertex in graph.vertices.values()] # vertices to process
heapq.heapify(queue)
s = [] # set of vertices with shortest path already determined
while queue:
u = heapq.heappop(queue) # extract min
s.append(u)
for v in u.edges:
graph_utils.relax(u, v, weights)
def dijkstra(graph, costs, start_vertex):
current_node = graph.vertices[start_vertex.key]
graph_utils.init_single_source(graph, current_node)
unvisited = set(graph.vertices[vertex] for vertex in graph.vertices)
while current_node:
neighbors = [vertex for vertex in current_node.edges
if vertex in unvisited]
for neighbor in neighbors:
if (current_node.d + costs[(current_node.key, neighbor.key)]) < neighbor.d:
neighbor.d = current_node.d + costs[(current_node.key, neighbor.key)]
unvisited.remove(current_node)
if not unvisited:
break
min_node = min(unvisited, key=lambda x: x.d)
current_node = min_node if min_node.d != float('inf') and min_node in unvisited else None
def dijkstra_2(graph, costs, start_vertex):
current_node = graph.vertices[start_vertex.key]
graph_utils.init_single_source(graph, current_node)
unvisited = set(graph.vertices[vertex] for vertex in graph.vertices)
while current_node:
neighbors = [
vertex for vertex in current_node.edges if vertex in unvisited
]
for neighbor in neighbors:
if (current_node.d +
costs[(current_node.key, neighbor.key)]) < neighbor.d:
neighbor.d = current_node.d + costs[(current_node.key,
neighbor.key)]
unvisited.remove(current_node)
if not unvisited:
break
min_node = min(unvisited, key=lambda x: x.d)
current_node = min_node if min_node.d != float(
'inf') and min_node in unvisited else None
