如何在CountVectorizer中对句子应用权重(多次计算每个句子的标记)

from sklearn.feature_extraction.text import CountVectorizer

vectorizer = CountVectorizer()

def calculate_weights(sentences):
    weights = []
    for sentence in sentences:
        # 多次计算每个句子的标记
        for _ in range(3):  # 假设计算3次
            # 将句子转换为向量
            vector = vectorizer.fit_transform([sentence])
            # 获取词汇表
            vocabulary = vectorizer.get_feature_names()
            # 获取每个词汇的出现次数
            counts = vector.toarray().flatten()
            # 计算每个词汇的权重
            sentence_weights = []
            for word, count in zip(vocabulary, counts):
                weight = count / len(vocabulary)  # 假设权重为词频除以词汇表长度
                sentence_weights.append((word, weight))
            weights.append(sentence_weights)
    return weights

sentences = ["This is the first sentence.", "This is the second sentence."]
weights = calculate_weights(sentences)