Use a hash map to store the Morse code for each letter, then iterate through the array, convert each word to its Morse representation, and add it to a set. Finally, return the size of the set as the count of distinct translations.
Pro tip: Clarify edge cases upfront, such as empty input or words with non-alphabetic characters, and discuss time/space complexity to demonstrate thoroughness.
Restate the problem in your own words and confirm assumptions, such as case insensitivity and handling of non-letter characters.
Propose using a mapping from letters to Morse codes and a set to track distinct translations. Explain why a set is ideal for deduplication.
State that the time complexity is O(N*L) where N is the number of words and L is the average word length, and space complexity is O(N*L) for storing the set.
Discuss how to handle empty input, words with mixed case, and non-alphabetic characters (e.g., skip or treat as empty).
Walk through a small example, such as ['gin', 'zen'], to verify that both map to the same Morse code and count as one distinct translation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem constraints (e.g., string length, dictionary size, expected output size) and then propose a recursive backtracking solution with memoization to avoid redundant computations. Explain how you would use a trie or hash set for efficient word lookups and analyze the time and space complexity, considering the exponential number of possible sentences.
Pro tip: Mention that the number of possible sentences can be exponential, so it's crucial to discuss output size and potential memory issues; also, consider using a trie for prefix pruning to optimize the search.
Ask about input sizes, character set, dictionary size, and whether the output order matters. Confirm that words can be reused and that we need all possible sentences.
Decide between a hash set for O(1) word lookups or a trie for prefix-based pruning. Discuss trade-offs: hash set is simpler but trie can reduce unnecessary checks.
Define a recursive function that builds sentences from a given index. Use memoization to cache results for each index to avoid recomputing overlapping subproblems.
Explain that time complexity is O(2^n) in the worst case due to exponential combinations, but memoization reduces redundant work. Space complexity includes recursion depth and output storage.
Walk through examples, including empty string, no valid sentences, and strings with many valid combinations. Discuss handling large outputs and potential memory limits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.