April 2017
Intermediate to advanced
532 pages
12h 39m
English
book
Machine Learning with Spark - Second Edition
by Rajdeep Dua, Brian O'Neill, Stephen Boesch, Manpreet Singh Ghotra, Nick Pentreath
Content preview from Machine Learning with Spark - Second Edition
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Vectors in Spark
Spark MLlib uses Breeze and JBlas for internal linear algebraic operations. It uses its own class to represent a vector defined using the org.apache.spark.mllib.linalg.Vector factory. A local vector has integer-typed and 0-based indices. Its values are stored as double-typed. A local vector is stored on a single machine, and cannot be distributed. Spark MLlib supports two types of local vectors, dense and sparse, created using factory methods.
The following code snippet shows how to create basic sparse and dense vectors in Spark:
val dVectorOne: Vector = Vectors.dense(1.0, 0.0, 2.0) println("dVectorOne:" + dVectorOne) // Sparse vector (1.0, 0.0, 2.0, 3.0) // corresponding to nonzero entries. val sVectorOne: Vector = Vectors.sparse(4, ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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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.