A teacher is writing a test with n true/false questions, with 'T' denoting true and 'F' denoting false. He wants to confuse the students by maximizing the number of consecutive questions with the same answer (multiple trues or multiple falses in a row).
You are given a string answerKey, where answerKey[i] is the original answer to the ith question. In addition, you are given an integer k, the maximum number of times you may perform the following operation:
'T' or 'F' (i.e., set answerKey[i] to 'T' or 'F').Return the maximum number of consecutive 'T's or 'F's in the answer key after performing the operation at most k times.
Example 1:
Input: answerKey = "TTFF", k = 2 Output: 4 Explanation: We can replace both the 'F's with 'T's to make answerKey = "TTTT". There are four consecutive 'T's.
Example 2:
Input: answerKey = "TFFT", k = 1 Output: 3 Explanation: We can replace the first 'T' with an 'F' to make answerKey = "FFFT". Alternatively, we can replace the second 'T' with an 'F' to make answerKey = "TFFF". In both cases, there are three consecutive 'F's.
Example 3:
Input: answerKey = "TTFTTFTT", k = 1 Output: 5 Explanation: We can replace the first 'F' to make answerKey = "TTTTTFTT" Alternatively, we can replace the second 'F' to make answerKey = "TTFTTTTT". In both cases, there are five consecutive 'T's.
Constraints:
n == answerKey.length1 <= n <= 5 * 104answerKey[i] is either 'T' or 'F'1 <= k <= nThis 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.
The function maxConsecutiveAnswers applies the sliding window technique twice: once assuming we change all 'F' to 'T' and once the reverse. It uses two counters, maxT and maxF, to track the number of changes made. As soon as the number of required changes exceeds the allowed k, the window is shrunk from the left. The maximum length of such a valid window is tracked and returned.
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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.
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.
Here we loop over each character possibility ('T' and 'F') and use a simple two-pointer technique to determine the length of the segment to convert. As pointers move, each change is applied and the window is adjusted to stay under the threshold for changes.
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Java
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Time Complexity: O(n), based on the two-pointer traversal.
Space Complexity: O(1), engaging a minimal variable set, bypassing auxiliary data structures.
| Approach | Complexity |
|---|---|
| Sliding Window Technique | Time Complexity: O(n), where n is the length of the answerKey string. We traverse the string once. |
| Two Pointer Technique | Time Complexity: O(n), based on the two-pointer traversal. |
Maximize the Confusion of an Exam | 3 Approaches | MICROSOFT | Leetcode-2024 | Explanation • codestorywithMIK • 6,698 views views
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