擅长:python、mysql、java
<p>如果共享管道,将会有更好的更新。但你的例子很简单-</p>
<pre><code>from sklearn.feature_extraction.text import CountVectorizer
vec = CountVectorizer(stop_words='english')
op = vec.fit_transform(['Tom likes blue.', 'Adam likes yellow.' ,'Ann likes red and blue'])
print(op.todense())
print(vec.vocabulary_)
</code></pre>
<p><strong>输出</strong></p>
<pre><code>[[0 0 0 1 1 0 1 0]
[1 0 0 0 1 0 0 1]
[0 1 1 1 1 1 0 0]]
{'tom': 6, 'likes': 4, 'blue': 3, 'adam': 0, 'yellow': 7, 'ann': 2, 'red': 5, 'and': 1}
</code></pre>