zoukankan      html  css  js  c++  java
  • 215. Kth Largest Element in an Array

    Find the kth largest element in an unsorted array. Note that it is the kth largest element in the sorted order, not the kth distinct element.

    Example 1:

    Input: [3,2,1,5,6,4] and k = 2
    Output: 5
    

    Example 2:

    Input: [3,2,3,1,2,4,5,5,6] and k = 4
    Output: 4

    Note: 
    You may assume k is always valid, 1 ≤ k ≤ array's length.

    Approach #1: C++. [priority_queue]

    class Solution {
    public:
        int findKthLargest(vector<int>& nums, int k) {
            priority_queue<int, vector<int>, greater<int>> pq;
            
            for (int i = 0; i < nums.size(); ++i) {
                if (pq.size() <= k) pq.push(nums[i]);
                else pq.pop(), pq.push(nums[i]);
            }
            if (pq.size() > k) pq.pop();
            return pq.top();
        }
    };
    

      

    Approach #2: Java. [quick select]

    class Solution {
        public int findKthLargest(int[] nums, int k) {
            int n = nums.length;
            int p = quickSelect(nums, 0, n-1, n-k+1);
            return nums[p];
        }
        
        int quickSelect(int[] a, int lo, int hi, int k) {
            int i = lo, j = hi, pivot = a[hi];
            while (i < j) {
                if (a[i++] > pivot) swap(a, --i, --j);
            }
            
            swap(a, i, hi);
            
            int m = i - lo + 1;
            
            if (m == k) return i;
            else if (m > k) return quickSelect(a, lo, i-1, k);
            else return quickSelect(a, i+1, hi, k-m);
        }
        
        void swap(int[] a, int i, int j) {
            int tmp = a[i];
            a[i] = a[j];
            a[j] = tmp;
        }
    }
    

    Analysis;

    In this case, we swap the elements to make the array ordered. If the number of minimum elements in front of the array equal to k, then return the position at the array. Otherwise we divide the array with the pivot, if m(the elements of minimum numbers in front of the array) bigger the k, we can find the right position in the smaller numbers partion, Otherwise finding in the bigger partion.

    Approach #3: Python.

    import heapq
    class Solution(object):
        def findKthLargest(self, nums, k):
            """
            :type nums: List[int]
            :type k: int
            :rtype: int
            """
            min_heap = nums[:k]
            heapq.heapify(min_heap)
            for i in range(k, len(nums)):
                if nums[i] > min_heap[0]:
                    heapq.heappop(min_heap)
                    heapq.heappush(min_heap, nums[i])
            return min_heap[0]
    

      

    永远渴望,大智若愚(stay hungry, stay foolish)
  • 相关阅读:
    Day 22 初识面向对象
    Day 21 内存处理与正则
    Day 20 常用模块(三)
    Day 18 常用模块(二)
    url解析
    jQuery---扩展事件
    jQuery---文档操作
    jQuery---属性操作
    jQuery---基本语法
    CSS---常用属性总结
  • 原文地址:https://www.cnblogs.com/h-hkai/p/10144648.html
Copyright © 2011-2022 走看看