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
Table of Contents (16 chapters)
Scientific Computing with Scala
About the Author
About the Reviewer

Basic Breeze data structures

In this section, we will explore the basic data structures used to build Breeze programs. We will also discuss basic arithmetic operations that you can perform on these structures. The structures themselves are what you might expect from a numerical computing framework. If you have used MATLAB or Python's NumPy, you will instantly recognize them. The main ones you will probably be using are DenseVector and DenseMatrix. The sparse counterparts of these are SparseVector and CSCMatrix. These are optimized for use with large vectors and matrices that contain many zero elements. You can expect certain operations with these to be faster if your vectors and matrices fulfill the scarcity and largeness requirements. How sparse and large your data structures have to be for these to be worthwhile is not hard to guess. If you are dealing with sparse data structure, you should experiment and see if using SparseVector or SparseMatrix works faster.


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