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

Obtaining data to visualize

First of all, we will need multi-dimensional data to visualize. It can be thought of as a collection of rows, each with an equal number of columns (or attributes). Sometimes these rows are called records, and they usually represent the properties of some real-world or other object. For example, rows can represent a person with columns for age, height, and so on. The position of a value in the record therefore has a meaning.

Each column represents an attribute. We have already considered this type of data multiple times through out the course of this book. An example of such a dataset is the data about Iris flowers that is very commonly used as a sort of data analysis "Hello, World!" This dataset has already been explored in this book. You can get it from the website given here:

Just save it to your project directory for it to be readily available for use in the programs we will write in this chapter. This dataset is...