一、题目描述
设计一个找到数据流中第 k 大元素的类(class)。注意是排序后的第 k 大元素,不是第 k 个不同的元素。
请实现 KthLargest 类:
KthLargest(int k, int[] nums) 使用整数 k 和整数流 nums 初始化对象。
int add(int val) 将 val 插入数据流 nums 后,返回当前数据流中第 k 大的元素。
示例:
输入:
[“KthLargest”, “add”, “add”, “add”, “add”, “add”]
[[3, [4, 5, 8, 2]], [3], [5], [10], [9], [4]]
输出:
[null, 4, 5, 5, 8, 8]
解释:
KthLargest kthLargest = new KthLargest(3, [4, 5, 8, 2]);
kthLargest.add(3); // return 4
kthLargest.add(5); // return 5
kthLargest.add(10); // return 5
kthLargest.add(9); // return 8
kthLargest.add(4); // return 8
二、题解
方法一:小顶堆
维护长度为k的小顶堆,堆顶元素即为第k大的元素
class KthLargest {
private PriorityQueue<Integer> heap = new PriorityQueue<>();
private int k;
public KthLargest(int k, int[] nums) {
this.k = k;
int n = Math.min(k,nums.length);
int i=0;
for(;i<n;i++){
heap.offer(nums[i]);
}
while(i<nums.length){
if(nums[i]>heap.peek()){
heap.poll();
heap.offer(nums[i]);
}
i++;
}
}
public int add(int val) {
if(heap.isEmpty()||heap.size()<k){
heap.offer(val);
}
else if(val>heap.peek()){
heap.poll();
heap.offer(val);
}
return heap.peek();
}
}
/**
* Your KthLargest object will be instantiated and called as such:
* KthLargest obj = new KthLargest(k, nums);
* int param_1 = obj.add(val);
*/
优化:
class KthLargest {
private PriorityQueue<Integer> heap = new PriorityQueue<>();
private int k;
public KthLargest(int k, int[] nums) {
this.k = k;
for(int num:nums){
add(num);
}
}
public int add(int val) {
heap.offer(val);
if(k<heap.size()){
heap.poll();
}
return heap.peek();
}
}
/**
* Your KthLargest object will be instantiated and called as such:
* KthLargest obj = new KthLargest(k, nums);
* int param_1 = obj.add(val);
*/