我们可以使用一个大小为 k 的优先队列来存储前 k 大的元素,其中优先队列的队头为队列中最小的元素,也就是第 k 大的元素。
在单次插入的操作中,我们首先将元素 val 加入到优先队列中。如果此时优先队列的大小大于 k,我们需要将优先队列的队头元素弹出,以保证优先队列的大小为 k。
var KthLargest = function (k, nums) {
this.k = k;
this.heap = new MinHeap();
for (const x of nums) {
this.add(x);
}
};
KthLargest.prototype.add = function (val) {
this.heap.offer(val);
if (this.heap.size() > this.k) {
this.heap.poll();
}
return this.heap.peek();
};
class MinHeap {
constructor(data = []) {
this.data = data;
this.comparator = (a, b) => a - b;
this.heapify();
}
heapify() {
if (this.size() < 2) return;
for (let i = 1; i < this.size(); i++) {
this.bubbleUp(i);
}
}
peek() {
if (this.size() === 0) return null;
return this.data[0];
}
offer(value) {
this.data.push(value);
this.bubbleUp(this.size() - 1);
}
poll() {
if (this.size() === 0) {
return null;
}
const result = this.data[0];
const last = this.data.pop();
if (this.size() !== 0) {
this.data[0] = last;
this.bubbleDown(0);
}
return result;
}
bubbleUp(index) {
while (index > 0) {
const parentIndex = (index - 1) >> 1;
if (this.comparator(this.data[index], this.data[parentIndex]) < 0) {
this.swap(index, parentIndex);
index = parentIndex;
} else {
break;
}
}
}
bubbleDown(index) {
const lastIndex = this.size() - 1;
while (true) {
const leftIndex = index * 2 + 1;
const rightIndex = index * 2 + 2;
let findIndex = index;
if (
leftIndex <= lastIndex &&
this.comparator(this.data[leftIndex], this.data[findIndex]) < 0
) {
findIndex = leftIndex;
}
if (
rightIndex <= lastIndex &&
this.comparator(this.data[rightIndex], this.data[findIndex]) < 0
) {
findIndex = rightIndex;
}
if (index !== findIndex) {
this.swap(index, findIndex);
index = findIndex;
} else {
break;
}
}
}
swap(index1, index2) {
[this.data[index1], this.data[index2]] = [
this.data[index2],
this.data[index1],
];
}
size() {
return this.data.length;
}
}