← Anthropic Interview Insights
Sounds trivial until you're actually writing it out.
Clarify the input format and edge cases, then propose a single-pass linear scan that groups consecutive identical function names into events with start and end indices. Discuss time and space complexity, and consider whether to use half-open intervals [start, end) as specified.
Pro tip: Mention that the end time is exclusive, so the interval length is end - start, and that the last event must be closed after the loop. Also note that if samples are at regular intervals, you can convert indices to timestamps by multiplying by the sampling period.
Confirm the input is a list of function names, one per tick, and that intervals are half-open [start, end). Ask about empty input, single sample, and whether timestamps are needed or just indices.
Use a single pass: initialize start index and current function from the first sample. Iterate from the second sample; when the function changes, emit an event for the previous function with interval [start, i), then update start and current function.
After the loop, emit the last event with interval [start, n), where n is the number of samples. This ensures the last event is captured.
State that the algorithm runs in O(n) time and O(1) extra space (excluding output). Mention that output size is O(n) in the worst case (alternating functions).
Walk through a small example like ['A', 'A', 'B', 'B', 'B', 'A'] to verify events: A [0,2), B [2,5), A [5,6). Also test edge cases: empty list, single element, all same function.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I tried to bolt this onto my existing loop instead of stepping back and thinking about it cleanly.
First, clarify the problem: you need to emit events only when a function appears in at least k consecutive samples, discarding shorter runs. Then, propose an efficient streaming algorithm that tracks the current run length for each function and emits an event when the run reaches k, handling the end of the stream appropriately.
Pro tip: Mention that you can emit the event as soon as the run length hits k, and then continue emitting for each subsequent sample in the same run, or buffer and emit at the end of the run—clarify which behavior is expected. Also, consider memory usage: if the number of functions is large, use a hash map to track run lengths.
Confirm what 'consecutive samples' means (e.g., in a time series or sequence) and whether the event should be emitted once when the run reaches k or for every sample in the run. Also, ask about the expected input format and whether k is fixed or variable.
Use a hash map to store the current consecutive count for each function. Iterate through the samples, updating the count for the current function and resetting counts for others (or only update the current function and reset when a different function appears).
When a function's count reaches k, emit an event. If the requirement is to emit for every sample in the run, continue emitting for each subsequent sample of the same function. If only once per run, emit only at the moment the count hits k.
At the end of the stream, if a run is ongoing but hasn't reached k, discard it. Also handle cases where k <= 0 (invalid) or k = 1 (emit every sample).
Discuss time complexity O(n) and space O(m) where m is the number of distinct functions. Compare with alternative approaches like buffering runs and filtering at the end, noting memory implications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.