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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.
1#include <stdio.h>
2
3int findPoisonedDuration(int* timeSeries, int timeSeriesSize, int duration) {
4 int totalDuration = 0;
5 for (int i = 0; i < timeSeriesSize; i++) {
6 if (i == timeSeriesSize - 1 || timeSeries[i + 1] >= timeSeries[i] + duration) {
7 totalDuration += duration;
8 } else {
9 totalDuration += timeSeries[i + 1] - timeSeries[i];
10 }
11 }
12 return totalDuration;
13}
14
15int main() {
16 int timeSeries[] = {1, 4};
17 int duration = 2;
18 int size = sizeof(timeSeries) / sizeof(timeSeries[0]);
19 printf("%d\n", findPoisonedDuration(timeSeries, size, duration));
20 return 0;
21}The solution iterates over the array, and for each attack time, it calculates the increment to the total poison duration. If the next attack occurs after the current poison effect ends, it simply adds the full duration. Otherwise, it adds only the period from the current attack time to the next one (since next attack resets the poison).
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.
1def
The Python code handles poisoning implementation using dynamic calculation of the current poison effect's end time. Adjustments are made for overlaps, and the final duration is computed efficiently.