Book Image

Mastering Go - Second Edition

By : Mihalis Tsoukalos
Book Image

Mastering Go - Second Edition

By: Mihalis Tsoukalos

Overview of this book

Often referred to (incorrectly) as Golang, Go is the high-performance systems language of the future. Mastering Go, Second Edition helps you become a productive expert Go programmer, building and improving on the groundbreaking first edition. Mastering Go, Second Edition shows how to put Go to work on real production systems. For programmers who already know the Go language basics, this book provides examples, patterns, and clear explanations to help you deeply understand Go’s capabilities and apply them in your programming work. The book covers the nuances of Go, with in-depth guides on types and structures, packages, concurrency, network programming, compiler design, optimization, and more. Each chapter ends with exercises and resources to fully embed your new knowledge. This second edition includes a completely new chapter on machine learning in Go, guiding you from the foundation statistics techniques through simple regression and clustering to classification, neural networks, and anomaly detection. Other chapters are expanded to cover using Go with Docker and Kubernetes, Git, WebAssembly, JSON, and more. If you take the Go programming language seriously, the second edition of this book is an essential guide on expert techniques.
Table of Contents (20 chapters)
Title Page

Anomaly detection

Anomaly detection techniques try to find the probability that a given set contains anomalous behavior, which can be unusual values or patterns.

The utility that is going to be developed in this section is called anomaly.go, and it is going to be presented in three parts. The utility uses probabilistic anomaly detection with the help of the Anomalyzer package and calculates the probability that the given set of numeric values contains anomalous behavior.

The first part of anomaly.go is as follows:

package main 
 
import ( 
    "flag" 
    "fmt" 
    "math/rand" 
    "strconv" 
    "time" 
 
    "github.com/lytics/anomalyzer" 
) 
 
func random(min, max int) int { 
    return rand.Intn(max-min) + min 
} 

The second part of anomaly.go is as follows:

func main() { 
    flag.Parse() 
    if len(flag.Args...