Book Image

Julia 1.0 Programming Cookbook

By : Bogumił Kamiński, Przemysław Szufel
Book Image

Julia 1.0 Programming Cookbook

By: Bogumił Kamiński, Przemysław Szufel

Overview of this book

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt

Implementing a custom pseudo-random number generator

In many situations in Julia, you might want to extend some abstract type defined in the base language. In this recipe, we will show how you can implement a simple pseudo-random number generator extending AbstractRNG.

Getting ready

In order to create your own pseudo-random number generator, you have to define a concrete type that is a subtype of the AbstractRNG abstract type and which implements methods for the seed!, rand, and rng_native_52 functions. In this recipe, we will show how you can achieve this.

The generator we will implement is called 64-bit Xorshift. It was proposed by George Marsaglia in the paper, Xorshift RNGs, published in the Journal of Statistical Software, Vol 8(2003), Issue 14.

Before running this recipe, make sure that you have the StatsBase.jl and BenchmarkTools.jl packages installed. If it is missing make sure that you have it by running the following commands:

julia> using Pkg

julia> Pkg.add("BenchmarkTools"...