问题 scikit-learn TfidfVectorizer是什么意思？

我正在阅读有关TfidfVectorizer的文章 履行 scikit-learn，我不明白该方法的输出，例如：

new_docs = ['He watches basketball and baseball', 'Julie likes to play basketball', 'Jane loves to play baseball']
new_term_freq_matrix = tfidf_vectorizer.transform(new_docs)
print tfidf_vectorizer.vocabulary_
print new_term_freq_matrix.todense()

输出：

{u'me': 8, u'basketball': 1, u'julie': 4, u'baseball': 0, u'likes': 5, u'loves': 7, u'jane': 3, u'linda': 6, u'more': 9, u'than': 10, u'he': 2}
[[ 0.57735027  0.57735027  0.57735027  0.          0.          0.          0.
   0.          0.          0.          0.        ]
 [ 0.          0.68091856  0.          0.          0.51785612  0.51785612
   0.          0.          0.          0.          0.        ]
 [ 0.62276601  0.          0.          0.62276601  0.          0.          0.
   0.4736296   0.          0.          0.        ]]

是什么？（例如：u'me'：8）：

{u'me': 8, u'basketball': 1, u'julie': 4, u'baseball': 0, u'likes': 5, u'loves': 7, u'jane': 3, u'linda': 6, u'more': 9, u'than': 10, u'he': 2}

这是一个矩阵还是只是一个向量？我无法理解告诉我输出的原因：

[[ 0.57735027  0.57735027  0.57735027  0.          0.          0.          0.
   0.          0.          0.          0.        ]
 [ 0.          0.68091856  0.          0.          0.51785612  0.51785612
   0.          0.          0.          0.          0.        ]
 [ 0.62276601  0.          0.          0.62276601  0.          0.          0.
   0.4736296   0.          0.          0.        ]]

任何人都可以更详细地解释这些输出吗？

谢谢！