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Scala for Data Science

You're reading from  Scala for Data Science

Product type Book
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Pages 416 pages
Edition 1st Edition
Languages
Author (1):
Pascal Bugnion Pascal Bugnion
Profile icon Pascal Bugnion

Table of Contents (22) Chapters

Scala for Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Scala and Data Science 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework Pattern Matching and Extractors Index

Basic Breeze data types


Breeze is an extensive library providing fast and easy manipulation of arrays of data, routines for optimization, interpolation, linear algebra, signal processing, and numerical integration.

The basic linear algebra operations underlying Breeze rely on the netlib-java library, which can use system-optimized BLAS and LAPACK libraries, if present. Thus, linear algebra operations in Breeze are often extremely fast. Breeze is still undergoing rapid development and can, therefore, be somewhat unstable.

Vectors

Breeze makes manipulating one- and two-dimensional data structures easy. To start, open a Scala console through SBT and import Breeze:

$ sbt console
scala> import breeze.linalg._
import breeze.linalg._

Let's dive straight in and define a vector:

scala> val v = DenseVector(1.0, 2.0, 3.0)
breeze.linalg.DenseVector[Double] = DenseVector(1.0, 2.0, 3.0)

We have just defined a three-element vector, v. Vectors are just one-dimensional arrays of data exposing methods...

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