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This approach involves iteratively calculating the poison effect duration from the attack times given in timeSeries. The idea is to maintain a running total of poison duration impacts caused by each attack, making adjustments for overlaps where a new attack resets the poison timer.
Time Complexity: O(n), where n is the number of attack times in timeSeries.
Space Complexity: O(1), as we only use a limited amount of extra space.
1def findPoisonedDuration(timeSeries, duration):
2 totalDuration = 0
3 for i in range(len(timeSeries)):
4 if i == len(timeSeries) - 1 or timeSeries[i + 1] >= timeSeries[i] + duration:
5 totalDuration += duration
6 else:
7 totalDuration += timeSeries[i + 1] - timeSeries[i]
8 return totalDuration
9
10# Example usage
11print(findPoisonedDuration([1, 4], 2))The Python implementation loops through the attack times while checking conditions for poison duration accumulation. Depending on whether the poison was reset or not, different amounts of time are added to the total on each iteration.
In this approach, we utilize a dynamic programming technique to handle overlapping intervals. The essence here is to consider the overlap between poison effects and adjust the ending intervals of these periods dynamically.
Time Complexity: O(n) since we iterate through timeSeries just once.
Space Complexity: O(1) due to using minimal additional memory.
1
JavaScript code manages the poisoned intervals efficiently using dynamic endpoints. Each new attack time either extends or resets the current poison period, yielding a good performance structure.