写出用Python实现一个广度优先搜索遍历多个环的图论拓扑结构的代码
时间: 2023-03-15 14:03:35 浏览: 64
import collectionsdef breadth_first_search(graph, root):
visited, queue = set(), collections.deque([root])
visited.add(root)
while queue:
vertex = queue.popleft()
for neighbour in graph[vertex]:
if neighbour not in visited:
visited.add(neighbour)
queue.append(neighbour)
return visited# 代码的中文翻译:import collectionsdef 广度优先搜索(图, 根节点):
visited, queue = set(), collections.deque([root])
visited.add(root)
while queue:
vertex = queue.popleft()
for neighbour in graph[vertex]:
if neighbour not in visited:
visited.add(neighbour)
queue.append(neighbour)
return visited
相关问题
用Python写出多个起点的环状图论模型遍历代码
以下是用Python编写多个起点的环状图论模型遍历代码的示例:
```python
from collections import deque
# 定义图的邻接表表示
graph = {
'A': ['B', 'D'],
'B': ['C', 'E'],
'C': ['A', 'F'],
'D': ['E', 'G'],
'E': ['F', 'H'],
'F': ['D', 'I'],
'G': ['H', 'A'],
'H': ['I', 'B'],
'I': ['G', 'C']
}
# 定义起点列表
start_nodes = ['A', 'B', 'C']
# 定义遍历函数
def traverse(graph, start_nodes):
visited = set()
queue = deque(start_nodes)
while queue:
node = queue.popleft()
if node not in visited:
visited.add(node)
print(node)
for neighbor in graph[node]:
queue.append(neighbor)
# 调用遍历函数
traverse(graph, start_nodes)
```
在这个示例中,我们使用了Python的collections模块中的deque来实现队列,使用set来存储已访问的节点。同时,我们定义了一个起点列表,遍历函数会从每个起点开始进行遍历。
一个图的广度优先和深度优先遍历的python代码
好的,下面是一个无向图的广度优先遍历和深度优先遍历的Python代码示例:
```python
from collections import deque
# 定义一个无向图
graph = {
'A': ['B', 'C', 'D'],
'B': ['A', 'E'],
'C': ['A', 'F'],
'D': ['A', 'G'],
'E': ['B'],
'F': ['C'],
'G': ['D']
}
# 广度优先遍历
def bfs(graph, start):
queue = deque()
visited = set()
queue.append(start)
while queue:
node = queue.popleft()
if node not in visited:
visited.add(node)
print(node)
for neighbor in graph[node]:
queue.append(neighbor)
# 深度优先遍历
def dfs(graph, start, visited=None):
if visited is None:
visited = set()
visited.add(start)
print(start)
for neighbor in graph[start]:
if neighbor not in visited:
dfs(graph, neighbor, visited)
# 测试
print("广度优先遍历:")
bfs(graph, 'A')
print("深度优先遍历:")
dfs(graph, 'A')
```
其中,广度优先遍历使用了队列`deque`,深度优先遍历使用了递归。在这个示例中,我们定义了一个无向图`graph`,以字典的形式表示图的邻接表。我们从顶点`A`开始遍历,先进行广度优先遍历,然后进行深度优先遍历,输出遍历的结果。