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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204
8 章 Toolz を使って小さなノートパソコンでビッグデータを処理する方法
IncrementalPCA
クラスは
sklearn.decomposition
に用意されており、モデルをトレーニングするに
はバッチサイズが 1 より大きい必要があります。データ点のストリームからバッチのストリームを
作成する方法は、
toolz.curried.partition
関数をご覧ください。
8.6
全ゲノムを基にマルコフモデルを作成する
では、元のコード例に戻りましょう。マルコフモデルとは、どのようなもので、なぜ役に立つの
でしょうか。
一般に、マルコフモデルと呼ばれるものは、システムがある状態に遷移する確率は「直前の状
態」だけに依存する、と仮定します。例えば、今晴れているなら、高い確率で明日も晴れるでしょ
う。昨日が雨だったことは関係ありません。この理論では、未来予測に必要なすべての情報が、現
在の状態に完全に組み込まれているのです。過去は無関係なのです。この仮定は、他の方法では解
決困難な問題を簡単化するのに役立ち、概してよい結果を生み出しています。例えば、携帯電話や
衛星通信の信号処理の多くは、マルコフモデルが担っています。
これから説明していきますが、ゲノム科学の文脈で言うと、あるゲノムの異なる機能領域は、類
似した状態間の異なる遷移確率を持っています。遷移確率を新たなゲノムで観測することで、これ
らの領域の機能について何らかの予測ができます。天気のたとえに戻ると、晴れの日から雨の日に
遷移する確率は、ロサンゼルスとロンドンではまったく異なります。したがって ...
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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.