Problem · Hash Table
Most-Frequent Next-Word Predictor
● Medium
GoogleFULLTIMEONSITE INTERVIEW
See Google hiring insightsGoogleFULLTIMEONSITE INTERVIEWProblem statement
Train a next-word predictor from one ordered array of case-sensitive tokens. Every adjacent pair contributes one observation from its first token to its second.
The problem statement continues
ProExamples
Example 1
trainingTokens = ["i","like","tea","i","like","coffee","i","like","tea"]queries = ["i","like"]return = ["like","tea"]The word after i is always like, while tea follows like twice and coffee once.
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