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
BFS
"""
# @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
