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This approach involves iterating over each element in the array and calculating the subarray sum for each index considering the radius. If there is an insufficient number of elements, return -1 for that index. The average is computed using integer division.
Time Complexity: O(n*k), where n is the size of the array.
Space Complexity: O(n), for the output array.
1function getAverages(nums, k) {
2 const n = nums.length;
3 const avgs = Array(n).fill(-1);
4 const subArraySize = 2 * k + 1;
5
6 for (let i = k; i < n - k; i++) {
7 let sum = 0;
8 for (let j = i - k; j <= i + k; j++) {
9 sum += nums[j];
10 }
11 avgs[i] = Math.floor(sum / subArraySize);
12 }
13
14 return avgs;
15}
16
17const nums = [7, 4, 3, 9, 1, 8, 5, 2, 6];
18const k = 3;
19console.log(getAverages(nums, k));The JavaScript solution also uses a simple double loop to compute and store averages, with handling for insufficient elements using -1 offset.
The sliding window approach helps optimize the brute force method by avoiding redundant calculations when sums overlap, significantly reducing the time complexity.
Time Complexity: O(n), as each element is added and removed from the sum at most once.
Space Complexity: O(n), for storing the results.
1
This JavaScript solution optimizes the average calculation using a sliding window technique, effectively lowering the time complexity while keeping the calculation straightforward.