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This approach uses a sliding window technique to efficiently calculate the sum of required elements. By maintaining a running sum for the window and updating it as you slide, you can achieve the necessary transformation in linear time. The key is to account for the circular nature of the array using modulo operations to wrap around indices.
Time Complexity: O(n), where n is the length of the `code` array. Each element is processed once with constant-time window updates.
Space Complexity: O(1) auxiliary space (excluding the output array).
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JavaScript's solution emulates a sliding window sum update to maintain efficiency in modifying the resultant values. By setting start and end with transformations for negative k scenarios, it navigates through indices seamlessly using a modulo operation.
This approach is straightforward but less efficient, involving a direct sum computation for each index by wrapping around using the modulo operator. Each element's circular context is individually recalculated, following conditions for the sign of k.
Time Complexity: O(n*k) with n as length of `code` and k an absolute value.
Space Complexity: O(1) extra space beyond output.
1function decrypt(code, k) {
2 const n = code.length;
3 const result = new Array(n).fill(0);
4
5 if (k === 0) return result;
6
7 for (let i = 0; i < n; i++) {
8 let sum = 0;
9 for (let j = 1; j <= Math.abs(k); j++) {
10 const index = k > 0 ? (i + j) % n : (i - j + n) % n;
11 sum += code[index];
12 }
13 result[i] = sum;
14 }
15
16 return result;
17}
18
19// Example usage
20let code = [5, 7, 1, 4];
21let k = 3;
22console.log(decrypt(code, k));
23In this JavaScript example, the solution calculates the sum by processing each index individually and adjusting it according to the value of k, while mindful of the array's loop-around with modulo calculations ensuring accurate sums.