October 2014
Intermediate to advanced
288 pages
6h 27m
Japanese
Content preview from 実践 機械学習システム
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3.2
前処理：共通する単語の出現回数を類似度として計測する
55
ステマーを利用した場合、ステミングを行った後のトークン化と正規化を、自分で行う必要がありま
す。ここではその代わりに、次のように
build_analyzer
を上書きすることで対応します。
>>>
import nltk.stem
>>>
english_stemmer = nltk.stem.SnowballStemmer('english')
>>>
class StemmedCountVectorizer(CountVectorizer):
...
def build_analyzer(self):
...
analyzer = super(StemmedCountVectorizer, self).build_analyzer()
...
return lambda doc: (english_stemmer.stem(w) for w in analyzer(doc))
>>>
vectorizer = StemmedCountVectorizer(min_df=1, stop_words='english')
ここでは、次の３つのことを行っています。
●
前処理の段階で、文書を小文字に変換します（これは親クラスで行われます）。
●
トークン化の段階で、全ての単語を抜き出します（これも親クラスで行われます）。
●
それぞれの単語をステム化された単語に変換します。
結果を見ると、imagesとimagingが同じ単語としてカウント
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.