python数据结构与算法——图的广度优先和深度优先的算法

根据*的伪代码实现:

广度优先BFS:

使用队列集合

标记初始结点已被发现,放入队列

每次循环从队列弹出一个结点

将该节点的所有相连结点放入队列,并标记已被发现

通过队列,将迷宫路口所有的门打开,从一个门进去继续打开里面的门,然后返回前一个门处

 """
procedure BFS(G,v) is
let Q be a queue
Q.enqueue(v)
label v as discovered
while Q is not empty
v ← Q.dequeue()
procedure(v)
for all edges from v to w in G.adjacentEdges(v) do
if w is not labeled as discovered
Q.enqueue(w)
label w as discovered
"""
def procedure(v):
pass def BFS(G,v0):
""" 广度优先搜索 """
q, s = [], set()
q.extend(v0)
s.add(v0)
while q: # 当队列q非空
v = q.pop(0)
procedure(v)
for w in G[v]: # 对图G中顶点v的所有邻近点w
if w not in s: # 如果顶点 w 没被发现
q.extend(w)
s.add(w) # 记录w已被发现

深度优先DFS

使用 集合

初始结点入栈

每轮循环从栈中弹出一个结点,并标记已被发现

对每个弹出的结点,将其连接的所有结点放到队列中

通过栈的结构,一步步深入挖掘

 """"
Pseudocode[edit]
Input: A graph G and a vertex v of G Output: All vertices reachable from v labeled as discovered A recursive implementation of DFS:[5] 1 procedure DFS(G,v):
2 label v as discovered
3 for all edges from v to w in G.adjacentEdges(v) do
4 if vertex w is not labeled as discovered then
5 recursively call DFS(G,w)
A non-recursive implementation of DFS:[6] 1 procedure DFS-iterative(G,v):
2 let S be a stack
3 S.push(v)
4 while S is not empty
5 v = S.pop()
6 if v is not labeled as discovered:
7 label v as discovered
8 for all edges from v to w in G.adjacentEdges(v) do
9 S.push(w)
""" def DFS(G,v0):
S = []
S.append(v0)
label = set()
while S:
v = S.pop()
if v not in label:
label.add(v)
procedure(v)
for w in G[v]:
S.append(w)
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