Book Image

Mastering Concurrency Programming with Java 9 - Second Edition

By : Javier Fernández González
Book Image

Mastering Concurrency Programming with Java 9 - Second Edition

By: Javier Fernández González

Overview of this book

Concurrency programming allows several large tasks to be divided into smaller sub-tasks, which are further processed as individual tasks that run in parallel. Java 9 includes a comprehensive API with lots of ready-to-use components for easily implementing powerful concurrency applications, but with high flexibility so you can adapt these components to your needs. The book starts with a full description of the design principles of concurrent applications and explains how to parallelize a sequential algorithm. You will then be introduced to Threads and Runnables, which are an integral part of Java 9's concurrency API. You will see how to use all the components of the Java concurrency API, from the basics to the most advanced techniques, and will implement them in powerful real-world concurrency applications. The book ends with a detailed description of the tools and techniques you can use to test a concurrent Java application, along with a brief insight into other concurrency mechanisms in JVM.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

First example - the k-nearest neighbors algorithm

The k-nearest neighbors algorithm is a simple machine learning algorithm used for supervised classification. The main components of this algorithm are:

  • A train dataset: This dataset is formed by instances with one or more attributes that define every instance and a special attribute that determines the label of the instance
  • A distance metric: This metric is used to determine the distance (or similarity) between the instances of the train dataset and the new instances you want to classify
  • A test dataset: This dataset is used to measure the behavior of the algorithm

When it has to classify an instance, it calculates the distance against this instance and all the instances of the train dataset. Then, it takes the k-nearest instances and looks at the tag of those instances. The tag with most instances is the tag assigned to the input instance.

In this chapter, we are going to work with the Bank Marketing dataset of the UCI Machine Learning Repository...