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 with Saddle

In this section, let's look through an example of how one would use Saddle for data analysis. In this case, we will use the IRIS dataset again. We used it when discussing data storage and retrieval. You can get the dataset from this website:

The data is stored in CSV format, where values are separated by commas. Download the the file into the folder you used to test the Saddle examples in this chapter. Values are arranged in rows of five. The first value is sepal length in centimeters, the second value is sepal width in centimeters, the third value is petal length in centimeters, the fourth value is petal width in centimeters, and the fifth value is the name of the flower. The first thing we will want to do is to read the data in to a Saddle Frame. We want a frame where rows are labeled with an integer key and the columns represent one of the five attributes. We will label the columns accordingly...