#### 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
Scientific Computing with Scala
Credits
www.PacktPub.com
Preface
Free Chapter
Introducing Scientific Computing with Scala
Storing and Retrieving Data
Numerical Computing with Breeze
Interactive Computing with ScalaLab
Parallel Programming in Scala
Cluster Computing Using Scala
Scientific Plotting with Scala
Visualizing Multi-Dimensional Data in Scala
Index

## Sammon mapping

Sammon mapping is a way of projecting multi-dimensional data to a lower dimensional space. The idea behind Sammon mapping is to create a projection in which distances between points are kept the same as in the higher-dimensional space. This is probably going to be the most complicated program we have explored in this book.

We will use information from the previous chapters. For example, we will use the optimization routines we explored in the chapter dedicated to the Breeze numerical computing library. Sammon mapping was proposed by John W. Sammon in 1968 with regard to data structure analysis. It is particularly suited for exploratory data analysis, since it maintains (or aims to maintain) the structure of the data after projecting it to a lower dimensional space. To understand how Sammon mapping of a dataset is calculated, see the formula here:

Let's look at it term by term. E is called Sammon's stress or Sammon's error. This is the value we will want to minimize. Indices...