Book Image

Mastering Julia - Second Edition

By : Malcolm Sherrington
Book Image

Mastering Julia - Second Edition

By: Malcolm Sherrington

Overview of this book

Julia is a well-constructed programming language which was designed for fast execution speed by using just-in-time LLVM compilation techniques, thus eliminating the classic problem of performing analysis in one language and translating it for performance in a second. This book is a primer on Julia’s approach to a wide variety of topics such as scientific computing, statistics, machine learning, simulation, graphics, and distributed computing. Starting off with a refresher on installing and running Julia on different platforms, you’ll quickly get to grips with the core concepts and delve into a discussion on how to use Julia with various code editors and interactive development environments (IDEs). As you progress, you’ll see how data works through simple statistics and analytics and discover Julia's speed, its real strength, which makes it particularly useful in highly intensive computing tasks. You’ll also and observe how Julia can cooperate with external processes to enhance graphics and data visualization. Finally, you will explore metaprogramming and learn how it adds great power to the language and establish networking and distributed computing with Julia. By the end of this book, you’ll be confident in using Julia as part of your existing skill set.
Table of Contents (14 chapters)

Higher dimensional vectors

We can extend the 3D vector to higher dimensions, in which case, we will need to use an array to store the vector’s components and pass the number of dimensions as a second parameter.

As an example, we will use a secondary package, StaticArrays, which provides an SVector structure to hold the components. The following is not a full definition, just defining sufficient operations to calculate the distance between two N-Vectors and give yet another estimate of pi:

module VNX
using StaticArrays
import Base: +, *, /, ==, <, >
import LinearAlgebra: norm, dot
export VecN, norm, dist
struct VecN
  sv::SVector;
end
sizeof(a::VecN) = length(a.sv)
sOK(a::VecN, b::VecN) =
(sizeof(a) == sizeof(b)) ? true : throw(BoundsError("Vector of 
different lengths"));
(+)(a::VecN, b::VecN) = [a.sv[i] + b.sv[i]
                      ...