Multinomial naive Bayes

Implement a single multinomial naive-Bayes prediction with Laplace smoothing. You are given class_counts, a dict mapping class label (string) to number of training docs in that class; feature_counts, a dict mapping class label to a dict of {word: count of that word across docs of the class}; vocab, the list of all words; and doc, the list of words in the document to classify. Using add-one (Laplace) smoothing, compute for each class the log score = ln(P(class)) + sum over words in doc of ln((count_of_word_in_class + 1) / (total_words_in_class + |vocab|)). Return the class with the highest log score; break ties by choosing the lexicographically smallest class label. Words in doc not in vocab are ignored.