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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161
6.3 脳データのラプラシアン
線虫の脳の神経回路図が描けましたね。原論文で解説されているように、感覚神経細胞から介在
神経細胞のネットワークを通じて運動神経細胞へ、上位下達式な処理の様子が可視化されていま
す。また、運動神経細胞が
2
つのグループにはっきり区別されています。
2
つのグループは線虫の
体節のうち、首（左）と体（右）に対応します。
6.3.1
演習：神経細胞の接続の近さを表す図を描く
上記のコードをどのように変更すれば、論文の図
2B
のように、神経細胞同士の接続の近さ
（
affinity
）によって神経細胞を配置した図を描けるでしょうか。
6.3.2
チャレンジ問題：疎行列を扱う線形代数
前出のコードは、
NumPy
配列を用いて行列を格納して必要な計算を行っています。これが可能
なのは、
300
ノード未満の小さなグラフを使っているためです。ところが、もっと大きなグラフの
場合には、この方法ではうまくいきません。
例えば、
1
万個以上のパッケージを含む
Python Package Index
、略称
PyPI
に列挙されたライ
ブラリの関係性を分析したいとします。このグラフのラプラシアン行列を保持するには、
8(100
×
10
3
)
2
＝
8
×
10
10
バイト、すなわち
80 GB
の
RAM
が必要です。さらに、隣接行列、対称隣接行
列、擬似逆行列、そして計算時に使われる
2
つの一時的な行列も加えると、
480 GB
に達し、多く
のデスクトップコンピュータのメモリ容量を超えてしまいます。
「自分のデスクトップは
512 GB
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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.