當(dāng)我嘗試使用 python networkx 總結(jié)文本文檔時(shí)，我得到了 PowerIterationFailedConvergence:(PowerIterationFailedConvergence(...), 'power iteration failed to converge within 100 iterations') 如下面的代碼所示。代碼“scores = nx.pagerank(sentence_similarity_graph)”中顯示的錯(cuò)誤def read_article(file_name):    file = open(file_name, "r",encoding="utf8")    filedata = file.readlines()    text=""    for s in filedata:        text=text+s.replace("\n","")        text=re.sub(' +', ' ', text) #remove space        text=re.sub('—',' ',text)        article = text.split(". ")     sentences = []    for sentence in article:#         print(sentence)        sentences.append(sentence.replace("[^a-zA-Z]", "").split(" "))    sentences.pop()    new_sent=[]    for lst in sentences:        newlst=[]        for i in range(len(lst)):            if lst[i].lower()!=lst[i-1].lower():                newlst.append(lst[i])            else:                newlst=newlst        new_sent.append(newlst)    return new_sentdef sentence_similarity(sent1, sent2, stopwords=None):    if stopwords is None:        stopwords = []     sent1 = [w.lower() for w in sent1]    sent2 = [w.lower() for w in sent2]     all_words = list(set(sent1 + sent2))     vector1 = [0] * len(all_words)    vector2 = [0] * len(all_words)     # build the vector for the first sentence    for w in sent1:        if w in stopwords:            continue        vector1[all_words.index(w)] += 1     # build the vector for the second sentence    for w in sent2:        if w in stopwords:            continue        vector2[all_words.index(w)] += 1     return 1 - cosine_distance(vector1, vector2)def build_similarity_matrix(sentences, stop_words):    # Create an empty similarity matrix    similarity_matrix = np.zeros((len(sentences), len(sentences)))