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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.
1def decrypt(code, k):
2 n = len(code)
3 result = [0] * n
4
5 if k == 0:
6 return result
7
8 for i in range(n):
9 total = 0
10 for j in range(1, abs(k) + 1):
11 index = (i + j) % n if k > 0 else (i - j + n) % n
12 total += code[index]
13 result[i] = total
14
15 return result
16
17# Example usage
18code = [5, 7, 1, 4]
19k = 3
20print(decrypt(code, k))
21The Python implementation applies a straightforward nested loop to compute the sum for each element's neighboring indices accordingly, flexibly supporting an index adjustment for k values in either direction using modulo arithmetic.