不同的图遍历方法包括深度优先搜索(DFS)和广度优先搜索(BFS)。下面是它们的区别和代码示例:
深度优先搜索(DFS):
代码示例(使用递归):
def dfs(graph, start, visited):
visited.add(start)
print(start, end=" ")
for neighbor in graph[start]:
if neighbor not in visited:
dfs(graph, neighbor, visited)
graph = {
'A': ['B', 'C'],
'B': ['D', 'E'],
'C': ['F'],
'D': [],
'E': ['F'],
'F': []
}
visited = set()
dfs(graph, 'A', visited)
广度优先搜索(BFS):
代码示例:
from collections import deque
def bfs(graph, start):
visited = set()
queue = deque([start])
visited.add(start)
while queue:
vertex = queue.popleft()
print(vertex, end=" ")
for neighbor in graph[vertex]:
if neighbor not in visited:
queue.append(neighbor)
visited.add(neighbor)
graph = {
'A': ['B', 'C'],
'B': ['D', 'E'],
'C': ['F'],
'D': [],
'E': ['F'],
'F': []
}
bfs(graph, 'A')
以上是深度优先搜索和广度优先搜索的区别和代码示例。在实际应用中,根据具体问题的要求和图的特点选择适合的遍历方法。
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