zoukankan      html  css  js  c++  java
  • leetcode 703. Kth Largest Element in a Stream & c++ priority_queue & minHeap/maxHeap

    703. Kth Largest Element in a Stream & c++ priority_queue & minHeap/maxHeap

    相关链接

    leetcode
    c++ priority_queue cplusplus
    c++ priority_queue cnblog

    背景知识

      堆是算法中常用的数据结构之一,其结构是完全二叉树,但实现的方法最常见的是使用数组;这里主要介绍小顶堆,其根元素最小,对于任何一个节点来说,他都比其后代要小;访问器根元素的时间为O(1);树的高度严格控制在 log(n) 以内,故每次插入元素的时间为 O(log(n)).

      在c++的STL中提供了现成的堆模板给我们使用;你需要引入头文件queue.h即可;
    具体使用如下

    #include<queue.h>
    
    using namespace std;
    
    int main()
    {
        //大顶堆
        priority_queue<int> maxHeap;
        //等价于
        priority_queue<int, vector<int>, less<int> > maxHeap1;
    
        //小顶堆
        priority_queue<int, vector<int>, greater<int> > maxHeap1;
    
    }
    

    描述

       Design a class to find the kth largest element in a stream. Note that it is the kth largest element in the sorted order, not the kth distinct element.

      Your KthLargest class will have a constructor which accepts an integer k and an integer array nums, which contains initial elements from the stream. For each call to the method KthLargest.add, return the element representing the kth largest element in the stream.

    Example:

    int k = 3;
    int[] arr = [4,5,8,2];
    KthLargest kthLargest = new KthLargest(3, arr);
    kthLargest.add(3); // returns 4
    kthLargest.add(5); // returns 5
    kthLargest.add(10); // returns 5
    kthLargest.add(9); // returns 8
    kthLargest.add(4); // returns 8
    Note:
    You may assume that nums' length ≥ k-1 and k ≥ 1.

    solution1

      用一个小顶堆保存最大的K个数字;根节点处为K个数字中最小的一个,也是所有数据中第k大的数字;每当有新元素进来的时候,拿走小顶堆中最小的元素;

    
    using namespace std;
    
    class KthLargest {
    public:
    	int k;
    	priority_queue<int, vector<int>, greater<int> > minHeap;
    public:
    	KthLargest(int k, vector<int>& nums) :minHeap(nums.begin(), nums.end()) {
    		this->k = k;
    	}
    
    	int add(int val) {
    		minHeap.push(val);
    		while (k < minHeap.size())
    			minHeap.pop();
    		return minHeap.top();
    	}
    };
    
    
    
    int main()
    {
    	priority_queue<int> maxHeap;
    	//priority_queue<int, vector<int>, less<int> > maxHeap;
    	priority_queue<int, vector<int>, greater<int> > minHeap;
    
    	int k = 3;
    	vector<int> arr = { 4,5,8,2 };
    	
    	KthLargest kthLargest =KthLargest(3, arr);
    	cout<<kthLargest.add(3);   // returns 4
    	cout << kthLargest.add(5);   // returns 5
    	cout << kthLargest.add(10);  // returns 5
    	cout << kthLargest.add(9);   // returns 8
    	cout << kthLargest.add(4);   // returns 8
    
    	system("pause");
    }
    
    blogs record our growth
  • 相关阅读:
    通过队列实现进程间的通信(使用阻塞方式调用func函数)
    Scrapy 项目:QuotesBot
    数据分析_找数据参考网站
    Matplotlib 图表绘制工具学习笔记
    Python算法_斐波那契数列(10)
    Python算法_排序数组(09)
    Python数据结构与算法_反转字符串(08)
    Python算法_爬楼梯(08)
    Python数据结构与算法_搜索插入位置(07)
    Python数据结构与算法_删除排序数组中的重复项(06)
  • 原文地址:https://www.cnblogs.com/qwfand/p/12610576.html
Copyright © 2011-2022 走看看