# AI solution for JavaScript Coding Question on BFE.dev

152. Find Top k Elements

One possible solution for this problem is to use a hash map to count the occurrences of each number in the array, and then use a priority queue to keep track of the top k numbers. The time complexity of this approach is O(n log k), where n is the length of the input array, due to the operations of inserting and removing elements from the priority queue. The space complexity is O(n), due to the hash map that stores the counts.

Here is the code for this solution:

`function topK(arr, k) { // create a hash map to count occurrences of each number const counts = new Map(); for (const num of arr) { counts.set(num, (counts.get(num) || 0) + 1); } // create a priority queue to store the top k numbers const queue = new PriorityQueue((a, b) => a[1] - b[1]); for (const [num, count] of counts.entries()) { queue.offer([num, count]); if (queue.size() > k) { queue.poll(); } } // extract the top k numbers from the priority queue const result = []; while (!queue.isEmpty()) { result.unshift(queue.poll()[0]); } return result;}class PriorityQueue { constructor(compare) { this.heap = []; this.compare = compare || ((a, b) => a - b); } size() { return this.heap.length; } offer(item) { this.heap.push(item); this.bubbleUp(this.heap.length - 1); } poll() { if (this.heap.length === 0) { return undefined; } const top = this.heap[0]; const bottom = this.heap.pop(); if (this.heap.length > 0) { this.heap[0] = bottom; this.bubbleDown(0); } return top; } bubbleUp(index) { while (index > 0) { const parent = (index - 1) >> 1; if (this.compare(this.heap[index], this.heap[parent]) < 0) { this.swap(index, parent); index = parent; } else { break; } } } bubbleDown(index) { const last = this.heap.length - 1; while (true) { const left = (index << 1) + 1; const right = (index << 1) + 2; let smallest = index; if (left <= last && this.compare(this.heap[left], this.heap[smallest]) < 0) { smallest = left; } if (right <= last && this.compare(this.heap[right], this.heap[smallest]) < 0) { smallest = right; } if (smallest !== index) { this.swap(index, smallest); index = smallest; } else { break; } } } swap(i, j) { const temp = this.heap[i]; this.heap[i] = this.heap[j]; this.heap[j] = temp; } isEmpty() { return this.heap.length === 0; }}`

As for improving the time and space complexity, one possible optimization is to use a hash map to keep only the top k elements and their counts, instead of storing all the counts in a separate map. This can reduce the space complexity to O(k), but the time complexity remains O(n log k), as the operations of finding, inserting and removing elements in the hash map can take up to O(log k) time.

Here is the code for this optimized solution:

`function topK(arr, k) { // create a hash map to keep the top k elements and their counts const counts = new Map(); for (const num of arr) { counts.set(num, (counts.get(num) || 0) + 1); if (counts.size > k) { let minCount = Infinity, minNum; for (const [num, count] of counts.entries()) { if (count < minCount) { minCount = count; minNum = num; } } counts.delete(minNum); } } // extract the top k numbers from the hash map const result = []; for (const [num, count] of counts.entries()) { while (count > 0) { result.push(num); count--; } } return result.reverse();}`

This optimized solution has a space complexity of O(k) and a time complexity of O(n log k), but it may perform faster than the previous solution for small values of k or arrays with many duplicates.