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This approach uses the sliding window technique to maintain a window of the answerKey where the number of modifications does not exceed k. We attempt to maximize the length of this window by evaluating either 'T' or 'F' transformations separately and then taking the maximum length between both transformations.
Time Complexity: O(n), where n is the length of the answerKey string. We traverse the string once.
Space Complexity: O(1), as we use constant space related to variables.
1def maxConsecutiveAnswers(answerKey, k):
2 def maxConsecutive(char):
3 left = maxLen = 0
4 count = 0
5 for right in range(len(answerKey)):
6 if answerKey[right] == char:
7 count += 1
8 while count > k:
9 if answerKey[left] == char:
10 count -= 1
11 left += 1
12 maxLen = max(maxLen, right - left + 1)
13 return maxLen
14
15 return max(maxConsecutive('T'), maxConsecutive('F'))
16
17answerKey = "TTFTTFTT"
18k = 1
19print(maxConsecutiveAnswers(answerKey, k))The Python solution respects the sliding window by evaluating both characters 'T' and 'F' distinctly through the nested maxConsecutive method. The method retains a count of the characters needing modification and retracts the left pointer when the count surpasses k.
In this approach, we utilize two pointers to explore the string sequence, adjusting pointers as we evaluate possibilities involving updates utilizing k. The purpose is to ensure the longest alteration window without exceeding k modifications. It proceeds by specifically examining alternate possibilities and then concluding with the optimal maximum length.
Time Complexity: O(n), based on the two-pointer traversal.
Space Complexity: O(1), engaging a minimal variable set, bypassing auxiliary data structures.
The Python variant distinctly analyzes answer conversions from separate character strains producing competitive responses at each tentative setting. Adjustments maintain plausible ranges to ensure altered strings sustain under k conversion restraint.