本文介绍了从一个数字亿检索前100号的处理方法,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 29岁程序员,3月因学历无情被辞! 我的一位朋友一直问一个问题One of my friend has been asked with a question从一个数字亿检索最大前100号在最近的一次面试。你有什么想法,拿出一个有效的方式来解决呢?in a recent job interview. Do you have any idea to come up with an efficient way to solve it?推荐答案运行它们都通过一个分堆:为每个输入号 K ,取代目前的分 M 与 MAX(K,M)。然后堆持有的100家最大的投入。Run them all through a min-heap of size 100: for each input number k, replace the current min m with max(k, m). Afterwards the heap holds the 100 largest inputs.像Lucene的搜索引擎可以使用这种方法,与改进,以选择最相关的搜索答案。A search engine like Lucene can use this method, with refinements, to choose the most-relevant search answers. 编辑:我失败了采访 - 我得到的信息错误的两倍(在做这件事之前,在生产后)。下面是code,以检查它;这是几乎相同的Python的标准 heapq.nlargest(): I fail the interview -- I got the details wrong twice (after having done this before, in production). Here's code to check it; it's almost the same as Python's standard heapq.nlargest():import heapqdef funnel(n, numbers): if n == 0: return [] heap = numbers[:n] heapq.heapify(heap) for k in numbers[n:]: if heap[0] < k: heapq.heapreplace(heap, k) return heap>>> funnel(4, [3,1,4,1,5,9,2,6,5,3,5,8])[5, 8, 6, 9] 这篇关于从一个数字亿检索前100号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! 上岸,阿里云! 08-13 17:30