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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.
1#include <stdio.h>
2#include <stdlib.h>
3
4int* getAverages(int* nums, int numsSize, int k, int* returnSize) {
5 *returnSize = numsSize;
6 int* avgs = (int*)malloc(numsSize * sizeof(int));
7 int subArraySize = 2 * k + 1;
8
9 for (int i = 0; i < numsSize; ++i) {
10 if (i < k || i >= numsSize - k) {
11 avgs[i] = -1;
12 } else {
13 long sum = 0;
14 for (int j = i - k; j <= i + k; ++j) {
15 sum += nums[j];
16 }
17 avgs[i] = sum / subArraySize;
18 }
19 }
20
21 return avgs;
22}
23
24int main() {
25 int nums[] = {7, 4, 3, 9, 1, 8, 5, 2, 6};
26 int numsSize = sizeof(nums) / sizeof(nums[0]);
27 int k = 3;
28 int returnSize;
29 int* averages = getAverages(nums, numsSize, k, &returnSize);
30
31 for (int i = 0; i < returnSize; ++i) {
32 printf("%d ", averages[i]);
33 }
34
35 free(averages);
36 return 0;
37}This C code implements the brute force approach where for each index, we calculate the sum of its k-radius subarray. If the index doesn't have enough elements on either side, it sets the result for that position to -1.
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.
1import
This Java implementation effectively uses sliding window optimization to keep a running total sum for the needed window range, achieving linear time complexity.