Python BFS与DFS搜索算法

最新推荐文章于 2024-08-13 13:47:59 发布

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#python

本文介绍了使用BFS（广度优先搜索）和DFS（深度优先搜索）算法在图中进行搜索，并展示了如何通过BFS实现最短路径查找。通过实例演示了这两种算法在字典表示的图结构中的应用。

BFS算法：利用队列进行搜索

DFS算法：利用栈进行搜索

1.BFS算法

##建立字典：表征该点的临接点
graph={
    "A":["B","C"],
    "B":["A","C","D"],
    "C":["A","B","D","F"],
    "D":["B","C","E","F"],
    "E":["C","D"],
    "F":["D"]
}

##BFS用队列解决搜索问题
def BFS(start_node):
    queue=[]
    queue.append(start_node)
    seen=set()
    seen.add(start_node)
    while len(queue)>0:
        now_node=queue.pop(0)
        for node in graph[now_node]:
            if node not in seen:
                queue.append(node)
                seen.add(node)
        print(now_node)

2.DFS算法

##DFS 用栈解决搜索问题
def DFS(start_node):
    stack=[]
    stack.append(start_node)
    seen=set()
    seen.add(start_node)
    while len(stack)>0:
        now_node=stack.pop()
        for node in graph[now_node]:
            if node not in seen:
                stack.append(node)
                seen.add(node)
        print(now_node)

#DFS("A")

3.最短路径

##求最短路径
def BFS(start_node):
    queue=[]
    queue.append(start_node)
    seen=set()
    seen.add(start_node)
    parent={start_node:None}
    while len(queue)>0:
        now_node=queue.pop(0)
        for node in graph[now_node]:
            if node not in seen:
                queue.append(node)
                parent[node]=now_node
                seen.add(node)
        #print(now_node)
    print(parent)

全部代码：

##建立字典：表征该点的临接点
graph={
    "A":["B","C"],
    "B":["A","C","D"],
    "C":["A","B","D","F"],
    "D":["B","C","E","F"],
    "E":["C","D"],
    "F":["D"]
}

##BFS用队列解决搜索问题
def BFS(start_node):
    queue=[]
    queue.append(start_node)
    seen=set()
    seen.add(start_node)
    while len(queue)>0:
        now_node=queue.pop(0)
        for node in graph[now_node]:
            if node not in seen:
                queue.append(node)
                seen.add(node)
        print(now_node)

#BFS("A")

##DFS 用栈解决搜索问题
def DFS(start_node):
    stack=[]
    stack.append(start_node)
    seen=set()
    seen.add(start_node)
    while len(stack)>0:
        now_node=stack.pop()
        for node in graph[now_node]:
            if node not in seen:
                stack.append(node)
                seen.add(node)
        print(now_node)

#DFS("A")

##求最短路径
def BFS(start_node):
    queue=[]
    queue.append(start_node)
    seen=set()
    seen.add(start_node)
    parent={start_node:None}##存储路径
    while len(queue)>0:
        now_node=queue.pop(0)
        for node in graph[now_node]:
            if node not in seen:
                queue.append(node)
                parent[node]=now_node
                seen.add(node)
        #print(now_node)
    print(parent)
##搜索A--E的路径
    v='E'
    while v!=None:
        print(v)
        v=parent[v]


BFS("A")