November 2018
Intermediate to advanced
272 pages
6h 51m
Japanese
book
エレガントなSciPy ―Pythonによる科学技術計算
by Juan Nunez-Iglesias, Stéfan van der Walt, Harriet Dashnow, 山崎 邦子, 山崎 康宏
Content preview from エレガントなSciPy ―Pythonによる科学技術計算
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117
5
章
疎行列を用いた分割表
疎（まばら）な音はいい。そのミニマリズム感が、何かすると打てば響くように、特別なものを生み出してい
く。僕はずっとそんな風にやっていくと思う。ただ、方法がわからないんだ。
―
ブリット・ダニエル（Spoon のリードボーカル）
現実世界の行列の多くは疎行列です。つまり値のほとんどがゼロだということです。
NumPy
配列を使って疎行列を処理しようとすれば、大量にあるゼロとの掛け算に膨大な時間と
エネルギーを費やすことになります。代わりに、
SciPy
の
sparse
モジュールを使えば、ゼロ以外
の値だけを見て、効率よく処理できます。
sparse
は、「正統な」疎行列問題を解くのに役立つのは
もちろんのこと、一見疎行列とは関係なさそうな問題にも使えます。
そんな問題の
1
つに、画像のセグメンテーションの比較があります（セグメンテーションの定
義は、「
3
章
ndimage
を使った画像領域のネットワーク」を復習してみてください）。
本章の話題を取り上げるきっかけとなった以下のコード例では、疎行列を
2
回利用しています。
最初に、
Andreas Mueller
が推薦したコードを使い、
2
つのセグメンテーション間のラベルの対応
を数える対応行列を計算します。続いて、
Jaime Fernández del Río
と
Warren Weckesser
の提案
に基づき、その対応行列を基に、セグメンテーション間の差を計る情報変化量というものを計算し
ます。
def variation_of_information(x, ...
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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.