基于python模拟bfs和dfs代码实例

bfs

"""
# @time  : 2020/11/8
# @author : jimou chen
"""


# 广搜
def bfs(graph, start):
  queue = [start] # 先把起点入队列
  visited = set() # 访问国的点加入
  visited.add(start)

  while len(queue):
    vertex = queue.pop(0)
    # 找到队列首元素的连接点
    for v in graph[vertex]:
      if v not in visited:
        queue.append(v)
        visited.add(v)
    # 打印弹出队列的该头元素
    print(vertex, end=' ')


if __name__ == '__main__':
  graph = {
    'a': ['b', 'd', 'i'],
    'b': ['a', 'f'],
    'c': ['d', 'e', 'i'],
    'd': ['a', 'c', 'f'],
    'e': ['c', 'h'],
    'f': ['b', 'h'],
    'g': ['c', 'h'],
    'h': ['e', 'f', 'g'],
    'i': ['a', 'c']
  }

  bfs(graph, 'a')

a b d i f c h e g
process finished with exit code 0

dfs

"""
# @time  : 2020/11/8
# @author : jimou chen
"""


# 深搜
def dfs(graph, start):
  stack = [start]
  visited = set()
  visited.add(start)

  while len(stack):
    vertex = stack.pop() # 找到栈顶元素
    for v in graph[vertex]:
      if v not in visited:
        stack.append(v)
        visited.add(v)

    print(vertex, end=' ')


if __name__ == '__main__':
  graph = {
    'a': ['b', 'd', 'i'],
    'b': ['a', 'f'],
    'c': ['d', 'e', 'i'],
    'd': ['a', 'c', 'f'],
    'e': ['c', 'h'],
    'f': ['b', 'h'],
    'g': ['c', 'h'],
    'h': ['e', 'f', 'g'],
    'i': ['a', 'c']
  }

  dfs(graph, 'e')

e h g f b a i d c
process finished with exit code 0

总结

很明显一个用了队列，一个用了栈

利用python语言优势，只需改动pop即可

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