August 2015
Intermediate to advanced
312 pages
7h 28m
Japanese
Content preview from 初めてのSpark
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229
11.4 データ型
Vector denseVec1 = Vectors.dense(1.0, 2.0, 3.0);
Vector denseVec2 = Vectors.dense(new double[] {1.0, 2.0, 3.0});
//
疎なベクトル
<1.0, 0.0, 2.0, 0.0>
の生成
; Vectors.sparse
はベクトルの
//
大きさ
(
ここでは
4)
と、
0
以外のエントリの位置と値を取る。
Vector sparseVec1 = Vectors.sparse(4, new int[] {0, 2}, new double[]{1.0, 2.0});
最後に、
Java
と
Scala
においては、
MLlib
の
Vector
クラスは主にデータを表現するためのもの
であり、ユーザーの
API
で加算や減算のような数学的な操作を提供するためのものではありませ
ん（
Python
では、もちろん
NumPy
を使って密なベクトルで計算を行い、
MLlib
に渡すことがで
きます）。そうなっているのは、主に
MLlib
を小規模にとどめるためであり、完全な線形代数ラ
イブラリを構築することは、
MLlib
のプロジェクトの範疇を超えているためです。ただし、プロ
グラム中でベクトル演算をしたい場合には、
Scala
なら
Breeze
（
https://github.com/scalanlp/
breeze
）、
Java
なら
MTJ
（
https://github.com/fommil/matrix-toolkits-java ...
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