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

Finding the index of a random minimum element in an array

In many applications, you need to find the index of the minimum element of some array. The built-in argmin function is designed to perform this task—it returns the index of the minimum element in a collection. However, if there are multiple minimal elements, then the first one will be returned. There are situations when we need to get all indices of a minimal element or a single index is chosen uniformly at random from this set. In this recipe, we discuss how you can implement such a function.

Getting ready

Make sure that you have the StatsBase.jl and BenchmarkTools.jl packages installed.

You can add them by running the following commands in the Julia command line:

julia> using Pkg; Pkg.add("StatsBase"); Pkg.add("BenchmarkTools")


In the GitHub repository for this recipe you will find the commands.txt file that contains the presented sequence of shell and Julia commands and the randargmin2.jl file that contains the source code of...