In this paper, spell check as an example to explain the Naive Bayes classifier is implemented. For a user to enter a word (words), the spelling checker tries to infer the most likely the correct word (correct). Of course, it is possible to enter the word itself is correct. For example, enter the word thew, users may want to enter the, there may be trying to enter the thaw. After To solve this problem, Naive Bayes classifier using a posterior probability P (c | w) to solve this problem. The P (c | w) represents the case of the probability of w c is inferred. In order to identify the most likely c, you should find out the maximum value of P (c | w), that is, to solve the problem

Naive bayes.py

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