Use a BFS-like simulation where each day you compute the next state of the grid based on the current state, counting infected neighbors for each susceptible cell. Continue until no cell changes, and return the number of days elapsed. Optimize by only checking cells that are susceptible and have at least one infected neighbor.
Pro tip: Clarify edge cases upfront: what if K=0 (all cells become infected immediately) or K>8 (no spread)? Also discuss time complexity and potential optimizations like using a queue for active cells.
Confirm the rules: infection spreads to susceptible cells with at least K infected neighbors among 8. Discuss edge cases like K=0, K>8, all infected initially, or no infected cells.
Decide between naive full-grid scan each day vs. optimized approach tracking only cells that could change. Explain trade-offs in time and space complexity.
For each day, compute the next state by checking each susceptible cell's 8 neighbors. Use a separate grid or in-place update with careful handling to avoid using updated values within the same day.
After each day, compare the new grid with the previous. If unchanged, stop and return the number of days. Otherwise, increment day count and continue.
Discuss time complexity O(days * M * N) and potential optimizations like using a queue of active cells or early termination when no new infections occur.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the grid as a state machine with susceptible, infected, and immune cells, tracking infection duration per cell. Simulate day by day, updating states based on neighbor infection and immunity timers, until no infected cells remain. Return the number of days elapsed.
Pro tip: Clarify edge cases upfront: initial infected cells, D=0, and whether immunity is permanent. Also discuss time/space complexity and potential optimizations like event-driven simulation or using a queue for infected cells.
Represent each cell as S, I, or Im. Define rules: S becomes I if adjacent to I; I becomes Im after D days; Im never changes.
Set initial infected cells and record infection start day for each. Use a 2D array for states and another for infection day or remaining days.
For each day, update states: newly infected cells from previous day's spread, and infected cells that reach D days become immune. Stop when no infected cells remain.
Count the number of days simulated until the infected count reaches zero. Return that count.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.