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import reimport requestsimport jiebafrom bs4 import BeautifulSoup as bpfrom nltk.classify import NaiveBayesClassifier# 爬取李白和杜甫的诗集urls = ['https://so.gushiwen.org/authors/authorvsw_b90660e3e492A{}.aspx','https://so.gushiwen.org/authors/authorvsw_515ea88d1858A{}.aspx']author = ['李白','杜甫']for index,url in enumerate(urls):    file = open('{}.txt'.format(author[index]),'w',encoding='utf8')    longstr = ''    for n in range(1,21):        urlp = url.format(n)        r = requests.get(urlp,)        bs1 = bp(r.text,'lxml')        contsons = bs1.select('div .contson')        for conston in contsons:            longstr += re.sub('\s|[，。]','',conston.text)    file.write(longstr)    file.close()text1 = open(r"李白.txt", "rb").read()list1 = jieba.cut(text1)text2 = open(r"杜甫.txt", "rb").read()list2 = jieba.cut(text2)# 数据准备libai = list1dufu = list2# 特征提取def word_feats(words):    return dict([(word, True) for word in words])libai_features = [(word_feats(lb), 'lb') for lb in libai]dufu_features = [(word_feats(df), 'df') for df in dufu]train_set = libai_features + dufu_features# 训练决策classifier = NaiveBayesClassifier.train(train_set)# 分析测试sentence = input("请输入一句你喜欢的诗：")print("\n")seg_list = jieba.cut(sentence)seg_list = [seg for seg in seg_list]# 统计结果lb = 0df = 0for word in seg_list:    classResult = classifier.classify(word_feats(word))    if classResult == 'lb':        lb = lb + 1    if classResult == 'df':        df = df + 1# 呈现比例x = float(str(float(lb) / len(seg_list)))y = float(str(float(df) / len(seg_list)))print('李白的可能性：%.2f%%' % (x * 100))print('杜甫的可能性：%.2f%%' % (y * 100))

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