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Description
方法一:最小堆
解题思想:
- 构建一个最小堆,并依次把数组的值插入堆中
- 当堆的容量超过 K,就删除堆顶
- 插入结束后,堆顶就是第K个最大的元素
代码:
最小堆的类:
class MinHeap {
constructor() {
this.heap = [];
}
getParentIndex(i) {
return (i - 1) >> 1;
}
getLeftIndex(i) {
return 2 * i + 1;
}
getRightIndex(i) {
return 2 * i + 2;
}
swap(i1, i2) {
const temp = this.heap[i1];
this.heap[i1] = this.heap[i2];
this.heap[i2] = temp;
}
shiftUp(index) {
if ( index === 0) return;
const parentIndex = this.getParentIndex(index);
if (this.heap[parentIndex] > this.heap[index]) {
this.swap(parentIndex, index);
this.shiftUp(parentIndex);
}
}
shiftDown(index) {
if (index > this.heap.length - 1) return;
const leftIndex = this.getLeftIndex(index);
const rightIndex = this.getRightIndex(index);
if (this.heap[leftIndex] < this.heap[index]) {
this.swap(leftIndex, index);
this.shiftDown(leftIndex);
}
if (this.heap[rightIndex] < this.heap[index]) {
this.swap(rightIndex, index);
this.shiftDown(rightIndex);
}
}
insert(value) {
this.heap.push(value);
this.shiftUp(this.heap.length - 1);
}
pop() {
this.heap[0] = this.heap.pop();
this.shiftDown(0)
}
peek() {
return this.heap[0];
}
size() {
return this.heap.length;
}
}
var findKthLargest = function(nums, k) {
const h = new MinHeap();
nums.forEach(num => {
h.insert(num);
if (h.size() > k) {
h.pop();
}
});
return h.peek();
};
复杂度分析:
- 时间复杂度:O(n*logK),遍历nums中的insert与pop的时间复杂度最大都是logK;
- 空间复杂度:O(K),K是堆的大小。