The streaming constraint is what tripped me up.
Start by clarifying requirements and constraints, then outline a streaming algorithm that processes characters one by one, applying normalization and filtering on the fly while updating a fixed-size count array. Emphasize the O(n) time and O(alphabet size) space guarantees, and discuss trade-offs around Unicode normalization and locale-specific handling.
Pro tip: Mention that full Unicode normalization (e.g., NFC/NFD) can be done incrementally per character using a streaming normalizer, but for English locales, a simpler approach of stripping combining marks after NFD is sufficient and avoids buffering the entire string.
Ask about the expected input size, character set (e.g., English vs. multilingual), and whether normalization should be locale-specific. Confirm that O(n) time and O(alphabet size) space are hard requirements.
Iterate over each character, apply case folding, skip whitespace/punctuation, and for English locales, decompose to NFD and drop combining marks. Update a count array (or hash map) for the resulting base characters.
Explain that full normalization can be done per character using a streaming normalizer, but for English, stripping combining marks after NFD is sufficient. Avoid materializing the full normalized string by processing characters on the fly.
After processing both strings, compare the count arrays. If they match, the strings are anagrams; otherwise, they are not. Ensure the comparison is O(alphabet size).
Address potential pitfalls: locale-specific case folding (e.g., Turkish i), handling of emojis or non-BMP characters, and the impact of normalization on performance. Mention that the alphabet size may be large for full Unicode, but for English it's small.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that anagram checking relies on character normalization and canonicalization, which must be locale-aware. Then outline a pipeline: apply Unicode normalization (NFKC/NFKD) with locale-specific tailoring, handle special cases like German ß via case folding and expansion, and ensure right-to-left scripts are processed in logical order without disrupting normalization. Emphasize that the core algorithm (sorting or counting) remains the same, but preprocessing must be robust and configurable.
Pro tip: Mention that Unicode case folding (not just lowercasing) is essential for correct anagram detection across locales, and that RTL scripts should be normalized in logical order—never visually reordered—to avoid corrupting the character sequence.
Ask whether the anagram checker must support all locales or just specific ones, and whether performance or memory is a concern. This shows you consider trade-offs before diving into implementation.
Use NFKC or NFKD normalization to decompose characters, then apply locale-specific rules (e.g., German ß → ss via case folding). Mention that ICU libraries provide this functionality.
Perform full Unicode case folding (e.g., ß → ss, ẞ → ss) rather than simple lowercasing. For other locales, consider ligatures (fi → fi) and diacritic removal if appropriate.
Ensure that normalization and subsequent processing occur on the logical character sequence, not the visual order. RTL scripts like Arabic or Hebrew should be handled by the same pipeline without reordering.
Test with locale-specific examples (e.g., 'straße' vs 'strasse') and RTL strings. Discuss performance implications of normalization and whether to cache normalized forms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
NaN handling is the annoying part because NaN != NaN in Python so a plain set won't catch it.
Start by clarifying requirements: lazy generator, stable order, custom key function, NaN handling, and complexity constraints. Then design a generator that maintains a set of seen keys, yielding elements only when a new key is encountered, and handle NaN by normalizing it to a sentinel value. Finally, discuss trade-offs and edge cases.
Pro tip: Emphasize that using a set for seen keys gives O(1) average lookup, but mention that if keys are unhashable, you might need a different approach. Also, highlight that NaN handling requires special care because NaN != NaN, so you must check for NaN explicitly.
Confirm that the generator should be lazy, stable, support a key function, and optionally treat NaN as equal. Discuss time and space complexity expectations.
Outline a generator function that iterates over the input, computes the key for each element, and checks if the key has been seen. If not, add to seen set and yield the element.
Incorporate the key function to transform elements before checking uniqueness. For NaN, detect if the key is NaN (using math.isnan) and replace with a unique sentinel to ensure all NaNs are treated as equal.
Explain that time complexity is O(n) due to single pass, and space is O(k) where k is number of distinct keys. Discuss potential issues with unhashable keys and alternatives like sorting or using a list for small inputs.
Mention testing with empty input, all duplicates, NaNs, custom key functions, and unhashable keys to ensure robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.