在有向树的广度优先搜索中跟踪深度 [英] Tracking depth in a breadth first search of a directed tree

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本文介绍了在有向树的广度优先搜索中跟踪深度的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!

问题描述

我正在尝试查找根与要遍历的节点深度之间的距离,例如,如果我有以下表示树 {1的邻接表:[2, 3],2:[4],3:[5]} 会创建如下所示的关联列表 [0、1、1、2、2] 表示每个节点的级别。

I'm trying to find the distance between the root and the depth of the node that is being traversed, for example if I had a the following adjancency list representing the tree { 1: [2, 3], 2: [4], 3: [5]} an associated list like the following would be created [0, 1, 1, 2, 2] denoting the level of each node.

我有以下代码,看不到要在何处添加计数功能等,理想情况下,也会处理后边缘和后边缘

I have the following code and can't see where I'm meant to add counting functionality etc, ideally this would deal with cross and back edges as well

def bfs(graph, root):
    seen, queue = set([root]), collections.deque([root])
    visit_order = []
    while queue:
        vertex = queue.popleft()
        visit_order.append(vertex)
        for node in graph[vertex]:
            if node not in seen:
                seen.add(node)
                queue.append(node)

    print(visit_order)


推荐答案

仅对节点进行排队,您可以将节点及其级别作为元组排队,并且当排队一个节点时,它总是与当前级别加一,因此当您将一个节点出队并将该节点附加到时visit_order 您还可以从元组中获取节点的级别:

Instead of queuing just the nodes, you can queue the nodes and their levels as tuples, and when you enqueue a node it's always coupled with the current level plus one, so that when you dequeue a node and append the node to visit_order you also get the level of the node from the tuple:

import collections
def bfs(graph, root):
    seen, queue = {root}, collections.deque([(root, 0)])
    visit_order = []
    levels = []
    while queue:
        vertex, level = queue.popleft()
        visit_order.append(vertex)
        levels.append(level)
        for node in graph.get(vertex, []):
            if node not in seen:
                seen.add(node)
                queue.append((node, level + 1))

    print(visit_order)
    print(levels)

这样:

bfs({ 1: [2, 3], 2: [4], 3: [5]}, 1)

将输出:

[1, 2, 3, 4, 5]
[0, 1, 1, 2, 2]

这篇关于在有向树的广度优先搜索中跟踪深度的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!

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