Sklearn Tfidf Vectorizer norm = None norm-l2

Question

您好我试图了解scikit-learn如何计算矩阵中的TFIDF分数：文档1，功能6，＆＃34; wine＆＃34;：

test_doc = ['The wine was lovely', 'The red was delightful',
            'Terrible choice of wine', 'We had a bottle of red']

# Create vectorizer
vec = TfidfVectorizer(stop_words='english')
# Feature vector
tfidf = vec.fit_transform(test_doc)

feature_names = vec.get_feature_names()
feature_matrix = tfidf.todense()

['bottle', 'choice', 'delightful', 'lovely', 'red', 'terrible', 'wine']
[[ 0.         0.         0.         0.78528828 0.        0.         0.6191303 ]
 [ 0.         0.         0.78528828 0.         0.6191303 0.         0.        ]
 [ 0.         0.61761437 0.         0.         0.        0.61761437 0.48693426]
 [ 0.78528828 0.         0.         0.         0.6191303 0.         0.        ]]

我正在使用一个非常相似的问题的答案为自己计算： How areTF-IDF calculated by the scikit-learn TfidfVectorizer但是在他们的TFIDFVectorizer中，norm = None。

由于我使用的是默认设置norm = l2，这与norm = None有何不同，我如何为自己计算？