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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130
5 章 疎行列を用いた分割表
べてみましょう。比較を公平にするため、
ndimage
には線形補間することを
order=1
で、元の形状
の外側のピクセルは無視することを
reshape=False
で指定します。
%timeit apply_transform(image, tf)
100 loops, average of 7: 3.35 ms +- 270 µs per loop (using standard deviation)
from scipy import ndimage as ndi
%timeit ndi.rotate(image, 30, reshape=False, order=1)
100 loops, average of 7: 19.7 ms +- 988 µs per loop (using standard deviation)
著者らのコンピュータでは、約
10
倍の高速化が見られます。この例では回転しか行いません
が、画像処理でレンズ歪みを補正したり、人の顔を変顔にするなどの複雑な変形処理も可能です。
一旦変換の計算ができれば、別の画像に繰り返し適用することも、疎行列代数のおかげで高速にで
きます。
SciPy
の疎行列の「標準的な」使い方がわかったところで、本章の着想をもたらした少し変わっ
た使い方を見てみましょう。
5.4
分割表再び
我々が、
SciPy
の疎行列形式を使って、疎な結合確率行列を手軽に構築しようとしていることを
思い出してください。
COO
形式は疎なデータを格納するのに、ゼロでない要素の行と列の座標と ...
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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.