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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136
5 章 疎行列を用いた分割表
prains = np.array([25, 27, 24, 18, 14, 11, 7, 8, 10, 15, 18, 23]) / 100
pshine = 1 - prains
p_rain_g_month = np.column_stack([prains, pshine])
# replace 'None' below with expression for nonconditional contingency
# table. Hint: the values in the table must sum to 1.
以下の
'None'
を条件付きではない分割表の表現で置き換える。
p_rain_month = None
ヒント：表中の値の総和は
1
にならなければならない。
# Add your code below to compute H(M|R) and H(M)
以下に
H(M|R)
と
H(M)
を計算する自分のコードを追加。
この
2
つの値は、合わせて、情報変化量（
variation of information
：
VI
）を定義します。
VI(A, B) = H(A|B) + H(B|A)
5.7
セグメンテーションにおける情報理論：情報変化量
画像のセグメンテーションの話に戻ると、「日」は「ピクセル」に、「雨」と「月」は「自動セグ
メンテーションのラベル（
S
）」と「正解データのラベル（
T
）」に対応します。すると、正解デー
タが与えられた場合の自動セグメンテーションの条件付きエントロピーは、あるピクセルの ...
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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.