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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
Languages
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Toc

Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 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 A. Pattern Matching and Extractors Index

Diving into Breeze


Let's get started. We will work in the Scala console, but a program similar to this example is available in BreezeDemo.scala in the examples corresponding to this chapter. Create a build.sbt file with the following lines:

scalaVersion := "2.11.7"

libraryDependencies ++= Seq(
  "org.scalanlp" %% "breeze" % "0.11.2",
  "org.scalanlp" %% "breeze-viz" % "0.11.2",
  "org.scalanlp" %% "breeze-natives" % "0.11.2"
)

Start an sbt console:

$ sbt console

scala> import breeze.linalg._
import breeze.linalg._

scala> import breeze.plot._
import breeze.plot._

scala> import breeze.numerics._
import breeze.numerics._

Let's start by plotting a sigmoid curve, . We will first generate the data using Breeze. Recall that the linspace method creates a vector of doubles, uniformly distributed between two values:

scala> val x = linspace(-4.0, 4.0, 200)
x: DenseVector[Double] = DenseVector(-4.0, -3.959798...

scala> val fx = sigmoid(x)
fx: DenseVector[Double] = DenseVector(0.0179862099620915...
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