要计算一个类型文档的词频和逆向文档频率（TF-IDF），可以使用Python的sklearn库。下面是一个示例代码：

from sklearn.feature_extraction.text import TfidfVectorizer

# 定义类型文档
documents = [
    "This is the first document.",
    "This document is the second document.",
    "And this is the third one.",
    "Is this the first document?",
]

# 创建一个TfidfVectorizer对象
vectorizer = TfidfVectorizer()

# 对文档进行拟合和转换
X = vectorizer.fit_transform(documents)

# 获取特征名字（单词）
feature_names = vectorizer.get_feature_names_out()

# 打印每个单词的词频和逆向文档频率
for i in range(len(documents)):
    print("Document:", i+1)
    for j in range(len(feature_names)):
        print("Word:", feature_names[j])
        print("   TF-IDF:", X[i, j])

运行上述代码，将打印出每个文档中每个单词的词频和逆向文档频率。请注意，词频和逆向文档频率的值是浮点数。