August 2015
Intermediate to advanced
312 pages
7h 28m
Japanese
Content preview from 初めてのSpark
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243
11.4 データ型
大きな行列群の場合、通常は完全な行列分解は不要であり、行列分解が必要になるのは、最大の
特異値とそれに関連する特異ベクトルのみです。これによってストレージが節約でき、ノイズを減
らすことができ、低ランクの行列構造を回復することができます。上位
k
個の特異値を保持するな
ら、結果の行列群の次元は、
U
：
m
×
k
、
Σ
：
k
×
k
、
V
：
n
×
k
となります。
特異値分解を行うには、
RowMatrix
に対して
computeSVD
を呼びます。
11-14
をご覧くだ
さい。
11-14
Scala
SVD
// RowMatrix
の
mat
内の上位
20
の特異値と、その特異ベクトルを計算する。
val svd: SingularValueDecomposition[RowMatrix, Matrix] =
mat.computeSVD(20, computeU=true)
val U: RowMatrix = svd.U // U
は分散
RowMatrix
。
val s: Vector = svd.s //
特異ベクトルはローカルで密なベクトル。
val V: Matrix = svd.V // V
はローカルで密な行列。
11.4.9
モデルの評価
機械学習のタスクで使うアルゴリズムが何であれ、モデルの評価は完結している機械学習のパイ
プラインにおける重要な部分です。多くの学習タスクは、多くのモデルを使って取り組むことが可
能であり、同じアルゴリズムであってさえ、パラメータの設定によって異なる結果が得られること ...
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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.