Book Image

Scientific Computing with Scala

By : Vytautas Jancauskas
Book Image

Scientific Computing with Scala

By: Vytautas Jancauskas

Overview of this book

Scala is a statically typed, Java Virtual Machine (JVM)-based language with strong support for functional programming. There exist libraries for Scala that cover a range of common scientific computing tasks – from linear algebra and numerical algorithms to convenient and safe parallelization to powerful plotting facilities. Learning to use these to perform common scientific tasks will allow you to write programs that are both fast and easy to write and maintain. We will start by discussing the advantages of using Scala over other scientific computing platforms. You will discover Scala packages that provide the functionality you have come to expect when writing scientific software. We will explore using Scala's Breeze library for linear algebra, optimization, and signal processing. We will then proceed to the Saddle library for data analysis. If you have experience in R or with Python's popular pandas library you will learn how to translate those skills to Saddle. If you are new to data analysis, you will learn basic concepts of Saddle as well. Well will explore the numerical computing environment called ScalaLab. It comes bundled with a lot of scientific software readily available. We will use it for interactive computing, data analysis, and visualization. In the following chapters, we will explore using Scala's powerful parallel collections for safe and convenient parallel programming. Topics such as the Akka concurrency framework will be covered. Finally, you will learn about multivariate data visualization and how to produce professional-looking plots in Scala easily. After reading the book, you should have more than enough information on how to start using Scala as your scientific computing platform

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Scientific Computing with Scala
Vytautas Jancauskas
Apr, 2016 | 232 pages
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Table of Contents (11 chapters)
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Introducing Scientific Computing with Scala
1. Introducing Scientific Computing with Scala
Why Scala for scientific computing?
Numerical computing packages for Scala
Data analysis packages for Scala
Other scientific software
Alternatives for doing plotting
Using Emacs as the Scala IDE
Profiling Scala code
Debugging Scala code
Building, testing, and distributing your Scala software
Mixing Java and Scala code
Summary
2
2. Storing and Retrieving Data
2. Storing and Retrieving Data
Reading and writing CSV files
Reading and writing JSON files
Reading and writing XML files
Database access using JDBC
Database access using Slick
Reading and writing HDF5 files
Summary
3
3. Numerical Computing with Breeze
3. Numerical Computing with Breeze
Using Breeze in your project
Basic Breeze data structures
Statistical computing with Breeze
Optimization
Signal processing
Cheat sheet
Summary
4
4. Using Saddle for Data Analysis
4. Using Saddle for Data Analysis
Installing Saddle
Basic Saddle data structures
Data analysis with Saddle
Summary
5
5. Interactive Computing with ScalaLab
5. Interactive Computing with ScalaLab
Installing and running ScalaLab
Basic ScalaSci data structures
Data storage and retrieval
Plotting with ScalaLab
Other ScalaLab features
Summary
6
6. Parallel Programming in Scala
6. Parallel Programming in Scala
Programming with Scala threads
Using Scala's parallel collections
Agent-based concurrency with the Akka framework
Summary
7
7. Cluster Computing Using Scala
7. Cluster Computing Using Scala
Using MPJ Express for distributed computing
Using an Akka cluster for distributed computing
Summary
8
8. Scientific Plotting with Scala
8. Scientific Plotting with Scala
Plotting with JFreeChart
Plotting with scala-chart
Plotting with Wisp
Summary
9
9. Visualizing Multi-Dimensional Data in Scala
9. Visualizing Multi-Dimensional Data in Scala
Obtaining data to visualize
Andrews curve
Parallel coordinates
Scatter plot matrix
Sammon mapping
Improving the program
Summary
10
Index
Index

Statistical computing with Breeze

Generating vectors and matrices of random values from a specified distribution is a very common task in most kinds of simulation software. The statistics-related stuff in Breeze is kept in the breeze.stats.distributions package. Before doing anything, we will need to import it:

scala> import breeze.stats.distributions._
import breeze.stats.distributions._

Now, to fill a vector with uniformly distributed values in the interval [0, 1], you can use the Uniform distribution:

scala> val u = Uniform(0.0, 1.0)
u: breeze.stats.distributions.Uniform = Uniform(0.0,1.0)

To draw a sample from a distribution, you use the sample method. You can specify the number of samples to draw. In which case, a vector will be returned:

scala> val sample = u.sample(5)
sample: IndexedSeq[Double] = Vector(0.314402432799765, 0.9148520439005925, 0.8929580216449953, 0.9447241194598741, 0.5203139296795614)

scala> val rv = DenseVector(sample)
rv: breeze.linalg.DenseVector[IndexedSeq...
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