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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