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

Data analysis packages for Scala

By data analysis packages, we mean software designed for analyzing data in some way. A simple statistical regression would be an example. Software implementing machine-learning algorithms would be another example.


Saddle is Scala's answer to R and Python's pandas package. It supports reading in structured data in a variety of different formats, including CSV and HDF5. The data can be loaded into frames and then manipulated as you would in other similar software. Statistical analysis can be performed, and you can build your own statistical analysis methods on top of the data structures provided by Saddle. Saddle is examined in detail in a separate chapter dedicated to it. It can be found at the following website:


Apache's MLlib library provides machine learning algorithms for the Spark platform. The library can be accessed from Scala as well as from Java and Python. It supports basic statistical methods for data analysis, various regression and classification methods, clustering via k-means, dimensionality reduction, and optimization methods. The number of algorithms in the library is constantly growing. The MLib library can be found at the following website: