def __init__(self, stopWords_path='../data/stop_words.txt', my_stopwords=None, max_df=0.5, max_features=1000, simi_threshold=0.5, res_save_path='./cluster_res.json'):
    self.stopwords = self.load_stopwords(stopWords_path)
    if isinstance(my_stopwords, list):
        self.stopwords += my_stopwords
    self.tfidf = TfidfVectorizer(stop_words=self.stopwords, max_df=max_df, max_features=max_features)
    self.simi_thr = simi_threshold
    self.cluster_center_vec = []
    self.idx_2_text = {}
    self.cluster_2_idx = {}
    self.res_path = res_save_path
def __init__(self, stopWords_path='../data/stop_words.txt', my_stopwords=None, max_df=0.5, max_features=1000, simi_threshold=0.5, res_save_path='./cluster_res.json'):
    self.stopwords = self.load_stopwords(stopWords_path)
    if isinstance(my_stopwords, list):
        self.stopwords += my_stopwords
    self.tfidf = TfidfVectorizer(stop_words=self.stopwords, max_df=max_df, max_features=max_features)
    self.simi_thr = simi_threshold
    self.cluster_center_vec = []
    self.idx_2_text = {}
    self.cluster_2_idx = {}
    self.res_path = res_save_path