Start by clarifying the requirements and edge cases, then outline the Luhn algorithm step-by-step. After validating the checksum, determine the card brand based on known prefix and length patterns, and return the appropriate result. Write clean, modular code with tests for edge cases.
Pro tip: Mention that you would strip non-digit characters and handle empty strings gracefully, and discuss how to extend the solution for new card brands without modifying core logic.
Ask about input format (spaces, hyphens), expected return values, and whether to handle unknown brands. Confirm that only digit-only strings are considered.
Describe the algorithm: starting from the rightmost digit, double every second digit, sum digits of products, and check if total modulo 10 is zero.
Use known prefixes and lengths: VISA starts with 4 (length 13/16/19), MASTERCARD starts with 51-55 or 2221-2720 (length 16), AMEX starts with 34 or 37 (length 15).
If Luhn fails, return INVALID_CHECKSUM. If Luhn passes but brand unknown, decide on a default (e.g., INVALID_BRAND or UNKNOWN). Otherwise return the brand.
Write unit tests for valid and invalid cases, including edge cases like empty string, single digit, and very long numbers. Discuss time/space complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Basically a one-liner extension of part 1.
First, clarify the existing function's behavior and the brand detection logic. Then, modify the control flow so that after a successful Luhn check, if no brand pattern matches, return UNKNOWN instead of an invalid result. Ensure that invalid Luhn checks still return the appropriate invalid status.
Pro tip: Mention that UNKNOWN should be a distinct return value from invalid, and consider how this change affects downstream consumers and tests. Also, discuss the importance of maintaining backward compatibility if the function is part of a public API.
Review the existing function to see how it currently handles Luhn validation and brand detection. Identify where the invalid result is returned.
Ensure that the Luhn check is performed first. If it fails, return the invalid result immediately.
After a successful Luhn check, attempt to match brand patterns. If no pattern matches, return UNKNOWN instead of invalid.
Add test cases for numbers that pass Luhn but match no brand, and update any documentation to reflect the new UNKNOWN return value.
Think about how this change impacts other parts of the system, such as API responses or error handling, and ensure consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem: define the card brands and their valid number patterns (e.g., length, prefixes). Then, for each brand, count how many numbers matching the brand's pattern also match the wildcard pattern, using combinatorics or digit DP. Finally, return the counts as brand-count pairs.
Pro tip: Mention that wildcard matching can be done efficiently with digit DP, but for fixed-length patterns, a simple per-position check suffices. Also, discuss how to handle overlapping brand patterns and ensure counts are exact.
Ask about the set of card brands, their validation rules (length, prefixes, Luhn check), and whether the wildcard pattern is fixed-length. Confirm the output format.
Represent each brand's valid numbers as a set of constraints (e.g., length, prefix ranges). This may involve pre-processing brand rules into a structured format.
For each brand, count the number of digit strings that satisfy both the brand's constraints and the wildcard pattern. Use combinatorics if constraints are simple, or digit DP for complex constraints.
Consider if a number can belong to multiple brands (e.g., due to overlapping prefixes). Decide whether to count it for each brand or resolve conflicts. Also handle patterns with no wildcards or all wildcards.
Output the counts as brand-count pairs, ensuring the order matches the expected format (e.g., sorted by brand name or as given).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem: the observed string has a trailing '?' indicating exactly one error (change, removal, addition, or transposition) occurred to the preceding digits. Then, systematically generate all possible original valid card numbers by reversing each error type, validate them using Luhn's algorithm and brand-specific patterns (e.g., Visa, Mastercard), and return the unique set with their brands.
Pro tip: Emphasize that the solution must be efficient and scalable, as card numbers can be long and multiple errors could be considered in production; also mention the importance of using Luhn's checksum and brand regexes to prune invalid candidates early.
Confirm the definition of 'valid' (Luhn checksum and brand-specific patterns) and the exact meaning of the trailing '?' (exactly one error of the specified types). Ask about input size and performance expectations.
For each error type, generate all possible original strings by reversing the error: for changed digit, try all 10 digits at each position; for removed digit, insert a digit at each position; for added digit, remove each digit; for transposition, swap each adjacent pair.
For each candidate, check if it passes Luhn's algorithm and matches a known card brand pattern (e.g., Visa starts with 4, Mastercard 51-55 or 2221-2720, etc.). Collect valid ones with their brands.
Remove duplicates (since different error reversals might yield the same original number) and return the list of unique valid card numbers with their brands.
Discuss time and space complexity: O(n * 10) for changes, O(n) for insertions/removals, O(n) for transpositions, where n is the length of the observed string. Mention potential optimizations like early pruning using brand prefixes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.