说到BFS,首先要介绍图
什么是图?图模拟一组链接,由节点和边组成
图分为有向图(directed graph)和无向图(undirected graph)
有向图中的关系是单向的,所以可以由箭头表示
无向图中直接相连的节点互为邻居,所以没有箭头
参考算法图解,例如,下面两个图是等价的。
那么广度优先搜索有什么用呢?它可以帮助回答下面两类问题:
- 从节点A出发,有前往节点B的路径吗?
- 从节点A出发,前往节点B的哪条路径最短
不过要想实现BFS,不仅要了解什么是图,还要了解队列(queue)这种数据结构
队列只有两种操作:入队和出队
扫描二维码关注公众号,回复:
4382920 查看本文章
队列是一种先进先出(First In First out,,FIFO)的数据结构,而栈是一种后进先出(Last In First Out,LIFO)的数据结构
我们的目的是在由这8个人组成的关系网中找到芒果销售商,那么谁是芒果销售商呢,假设人的姓名是否以m结尾:如果是,他就是芒果销售商,很明显图中就是Thom,接下由代码来实现它。
接下来是代码部分:
首先是实现图的代码,它是由散列表来实现的,在python中由字典来表现这种数据结构
graph = {}
graph['you'] = ['alice','bob','claire']
graph['bob'] = ['anuj','peggy']
graph['alice'] = ['peggy']
graph['claire'] = ['thom','jonny']
graph['anuj'] = []
graph['peggy'] = []
graph['thom'] = []
graph['jonny'] = []
print(graph)
然后是实现队列的代码
from collections import deque
search_queue = deque() #创建一个队列
search_queue += graph['you']
print(search_queue)
最后是算法的工作原理以及完整的BFS代码
#BFS
from collections import deque
graph = {}
graph['you'] = ['alice','bob','claire']
graph['bob'] = ['anuj','peggy']
graph['alice'] = ['peggy']
graph['claire'] = ['thom','jonny']
graph['anuj'] = []
graph['peggy'] = []
graph['thom'] = []
graph['jonny'] = []
def BFS(name):
search_queue = deque()
search_queue += graph[name]
searched = []
while search_queue:
person = search_queue.popleft()
if not person in searched:
if person_is_seller(person):
print(person+"is a mango seller!")
return True
else:
search_queue += graph[person]
searched.append(person)
return False
def person_is_seller(name):
return name[-1] == 'm'
if __name__ == '__main__':
BFS('you')