Parts 1 and 2 are basically table lookup plus a split call.
Start by clarifying the encoding and decoding requirements, including the delimiter used and whether the Morse code is standard. Then, implement a bidirectional mapping between letters and Morse code, and handle edge cases such as spaces and invalid input. For decoding, split the Morse string by the delimiter and map each code back to its corresponding letter.
Pro tip: Mention that Morse code is not a prefix code, so decoding without delimiters is ambiguous; always confirm the delimiter. Also, discuss how to handle spaces between words, often represented by a different delimiter like '/'.
Ask about the delimiter between Morse characters (e.g., space) and between words (e.g., '/'), and whether the input is case-sensitive. Confirm if the Morse code is standard ITU.
Create a dictionary mapping each letter (A-Z) to its Morse code and vice versa. Include digits and punctuation if needed.
Iterate over each character in the input string, convert to uppercase, look up its Morse code, and join with the character delimiter. Use a different delimiter for spaces between words.
Split the Morse string by the word delimiter first, then split each word by the character delimiter. Map each Morse code back to its letter and join to form the original text.
Test with empty strings, lowercase input, spaces, and invalid Morse codes. Discuss error handling for unknown characters.
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Treat this as a segmentation problem: use dynamic programming to determine if the Morse string can be partitioned into valid Morse codes from the vocabulary, then reconstruct the unique word. Discuss the DP state, transition, and how to handle potential ambiguity by checking for multiple valid segmentations.
Pro tip: Clarify whether the vocabulary is a set of words or Morse codes, and whether the Morse string is guaranteed to have exactly one valid segmentation. This shows you think about edge cases and problem constraints before coding.
Confirm that the vocabulary contains words, each with a known Morse encoding, and that the input is a continuous Morse string with no delimiters. Ask if the solution should return the word or indicate ambiguity.
Let dp[i] be true if the prefix of length i can be segmented into valid Morse codes. For each i, check all j < i such that the substring from j to i matches a Morse code in the vocabulary, and dp[j] is true.
During DP, store the index of the previous split and the corresponding word. After filling dp, backtrack from the end to build the source word.
If multiple segmentations exist, discuss how to detect and handle them (e.g., return any, or indicate ambiguity). Analyze time complexity: O(n * m) where n is Morse string length and m is max Morse code length.
Walk through a small example, consider edge cases (empty string, no valid segmentation), and mention possible optimizations like using a trie for Morse codes to speed up matching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The interviewer mentioned after the round that most candidates don't reach this part, which made me feel slightly better about my shaky implementation.
First, clarify the problem: given a Morse code string and a mapping from Morse codes to vocabulary words, find all sequences of words whose concatenated Morse equals the input. Then, design a backtracking algorithm that recursively tries each possible word at each position, pruning when no word matches the remaining prefix. Finally, discuss complexity, optimization (e.g., memoization, trie), and trade-offs.
Pro tip: Mention that you would use a trie or hash map for efficient lookup of Morse codes to words, and that memoization can avoid redundant exploration of suffixes, especially if the vocabulary is large.
Confirm the input format, the Morse-to-word mapping, and whether words can be reused. Ask about the expected size of input and vocabulary to guide optimization.
At each index in the Morse string, try every vocabulary word whose Morse code matches the substring starting at that index. Recursively proceed from the next index, accumulating the current sequence.
Preprocess the vocabulary into a trie keyed by Morse code or a hash map from Morse code to list of words. This allows O(1) or O(L) lookup per word, where L is the word's Morse length.
Analyze time complexity: O(N * W * L) worst-case, where N is input length, W is vocabulary size, L is max word length. Use memoization to cache results for suffixes to avoid recomputation.
Compare backtracking with dynamic programming. Mention that if only the count is needed, DP is more efficient; if all sequences are required, backtracking with memoization is suitable. Also discuss space complexity and potential for iterative deepening.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.