Pretty straightforward once you just hardcode the 26-letter mapping and throw everything into a set.
Use a hash set to store the Morse code representations of each word, then return the size of the set. For each word, map each character to its Morse code using a precomputed dictionary and concatenate the codes.
Pro tip: Mention that the Morse code for each letter is fixed and can be precomputed in a dictionary for O(1) lookup, and that using a set automatically handles duplicates. Also, note that the time complexity is O(N*L) where N is the number of words and L is the average word length, which is optimal.
Confirm that each letter maps to a unique Morse code string, and that we need to count distinct concatenated strings. Ask if the input array can be empty or contain empty strings.
Create a dictionary mapping each lowercase English letter to its Morse code. This allows O(1) lookup per character.
For each word, iterate through its characters, look up the Morse code, and concatenate them into a single string. Add this string to a hash set.
After processing all words, the size of the hash set gives the number of distinct Morse code strings.
State that the time complexity is O(N*L) where N is the number of words and L is the average length, and space complexity is O(N*L) for the set. This is optimal as we must read all characters.
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., dictionary size, string length, expected output size) and then propose a dynamic programming solution that builds sentences from left to right, using memoization to avoid redundant computations. Discuss the time and space complexity, and mention potential optimizations like using a trie for dictionary lookups or pruning branches when no words match.
Pro tip: Emphasize that the number of valid sentences can be exponential, so it's crucial to discuss output-sensitive complexity and consider whether to return all solutions or just count them. Also, mention that in practice, you might use a trie to speed up word lookups, which is a common optimization at Amazon-scale.
Ask about input sizes, dictionary size, whether words can be reused, and if the output should be all possible sentences or just a count. Confirm if the dictionary is static or dynamic.
Define a function that returns all valid sentences for a substring starting at index i. For each word in the dictionary that matches a prefix of the substring, recursively solve for the remainder and concatenate.
Use memoization to cache results for each starting index to avoid recomputing the same subproblems. This reduces time complexity from exponential to polynomial in the number of subproblems times the work per subproblem.
Discuss time complexity: O(n * m * L) where n is string length, m is dictionary size, and L is average word length, but output size can be exponential. Space complexity includes memoization and output storage.
Mention using a trie for dictionary lookups to reduce matching time, or pruning when no words match. Also, discuss iterative DP vs. recursive with memoization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.