emoticons = [":'(", ";)", ":p", ":s", "(:"]
def get_w_w_e(doc,emo):
    words = set()
    for word in re.split('\\W+', doc):
        if 2 < len(word) < 20:
            words.add(word.lower())
    for word in re.split(' ',doc):
        if word in emo:
            words.add(word)
    return words
def helper_fun(doc):
    return get_w_w_e(doc,emoticons)
if __name__ == '__main__':
    sample_ind = int(input())
    test_case=data.pop(sample_ind)
    c1=NaiveBayes(get_words)
    c2=NaiveBayes(helper_fun)
    for x in data:
        c1.train(x[1],x[0])
        c2.train(x[1],x[0])
    print(test_case[1])
    print(f'Vistinska klasa: {test_case[0]}')
    print(f'Klasa predvidena so Naiven Baes (bez emotikoni): {c1.classify_document(test_case[1])}')
    print(f'Klasa predvidena so Naiven Baes (so emotikoni): {c2.classify_document(test_case[1])}')