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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