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Julia 1.0 Programming Cookbook
Julia 1.0 Programming Cookbook

Julia 1.0 Programming Cookbook: Over 100 numerical and distributed computing recipes for your daily data science work?ow

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Julia 1.0 Programming Cookbook

Data Structures and Algorithms

In this chapter, we will cover the following recipes:

  • Finding the index of a random minimum element
  • Fast matrix multiplication
  • Implementing a custom pseudo-random number generator
  • Parsing Git logs with regular expressions
  • Non-standard ways to sort your data
  • Creating a function preimage - understanding how dictionaries and sets work
  • Working with UTF-8 strings

Introduction

Julia is shipped with a vast array of utility functions built into the core language and its standard libraries. Also, because of its speed, it is very well suited for the implementation of custom algorithms (as opposed to many other high-level languages, where users compose the analysis solely by using predefined algorithms from installed packages).

In this chapter, we show practical examples of how such custom algorithms can be implemented, while also taking advantage of the inbuilt functionality. The range of recipes shows that you can often implement your own low-level algorithm that is much faster than using standard functions (the recipe for finding the index of a random minimum element in an array) and that you can easily modify how standard operations work by overriding them with custom behavior (the matrix multiplication optimization recipe). Finally, we...

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...

Fast matrix multiplication

Performing computations on matrices are one of the fundamental operations in numerical computing. In particular, if we want to multiply an  matrix by an  matrix, this operation has  complexity, producing an  matrix. Therefore, if we want to multiply several matrices in a chain, the cost of this operation depends on the sequence in which we perform the multiplications.

For example, assume that we have the following matrices:

  • A having dimensions 10 x 40
  • B having dimensions 40 x 10
  • C having dimensions 10 x 50

And, we want to compute A*B*C. We can perform the computation either like this, (A*B)*C, or like this, A*(B*C). The cost of the first approach is proportional to 10*40*10+10*10*50=9000. The cost of the second approach is 40*10*50+10*40*50=40000. It is therefore evident that the order of operations has a significant...

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...

Parsing Git logs with regular expressions

One of the very common tasks in data science is parsing logs produced by some application. In this recipe, we will write a simple snippet that presents how we can analyze the contributions of committers to the Git repository.

Getting ready

In order to run this recipe, you need to have the DataFrames.jl and DataFramesMeta.jl packages installed. If they are missing run the following commands to add them:

julia> using Pkg

julia> Pkg.add("DataFrames")

julia> Pkg.add("DataFramesMeta")

Also, you need to have Git installed. You can get it from https://git-scm.com/.

When you run the git log --stat command on a repository, it prints output that looks similar...

Non-standard ways to sort your data

Sorting is one of the basic operations commonly performed when processing data. In this recipe, we will explore several options for how you can perform sorting in non-standard cases. In particular, we will compare the performance of the various options that can be used for sorting.

Getting ready

In this recipe, we want to sort rows of an array of Float64 numbers by their norms. For our purposes, the norm of a list of values  is defined as , that is, the Euclidean norm (see http://mathworld.wolfram.com/MatrixNorm.html).

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

Creating a function preimage - understanding how dictionaries and sets work

In this recipe, we will explain in detail how the Dict and Set types identify keys in Julia.

Getting ready

The example that we will use is the creation of a dictionary that stores a preimage function.

Given a function,  and , we will call preimage of  a set ; see the example at http://mathworld.wolfram.com/Preimage.html.

In particular, we need to create preimage of all possible values of a mapping, , given the domain, and the set B defined as an image of A under , as calculated by Julia.

In the GitHub repository for this recipe, you will find the commands.txt file...

Working with UTF-8 strings

Julia supports handling UTF-8 strings. However, the way you work with them is slightly different from such languages as R or Python. In this recipe, you will discover more about the String and SubString types, as well as the correct method of indexing into a string in Julia.

We will learn this by parsing a file containing the word Hello, written in different languages.

Getting ready

In this recipe we will work with text stored in the file hello.txt that has the following contents as shown in the following screenshot:

Some terminals, in particular, the standard Windows terminal started by cmd, might have problems with displaying some of the characters in this recipe. If you encounter...
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Key benefits

  • Address the core problems of programming in Julia with the most popular packages for common tasks
  • Tackle issues while working with Databases and Parallel data processing with Julia
  • Explore advanced features such as metaprogramming, functional programming, and user defined types

Description

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

Who is this book for?

The target audience of this book is data scientists or programmers that want to improve their skills in working with the Julia programming language. It is recommended that the user has a little experience with Julia or intermediate-level experience with other programming languages such as Python, R, or MATLAB.

What you will learn

  • Boost your code's performance using Julia's unique features
  • Organize data in to fundamental types of collections: arrays and dictionaries
  • Organize data science processes within Julia and solve related problems
  • Scale Julia computations with cloud computing
  • Write data to IO streams with Julia and handle web transfer
  • Define your own immutable and mutable types
  • Speed up the development process using metaprogramming

Product Details

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Length: 460 pages
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Language : English
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Edition : 1st
Language : English
ISBN-13 : 9781788998369
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Tools :

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Table of Contents

11 Chapters
Installing and Setting Up Julia Chevron down icon Chevron up icon
Data Structures and Algorithms Chevron down icon Chevron up icon
Data Engineering in Julia Chevron down icon Chevron up icon
Numerical Computing with Julia Chevron down icon Chevron up icon
Variables, Types, and Functions Chevron down icon Chevron up icon
Metaprogramming and Advanced Typing Chevron down icon Chevron up icon
Handling Analytical Data Chevron down icon Chevron up icon
Julia Workflow Chevron down icon Chevron up icon
Data Science Chevron down icon Chevron up icon
Distributed Computing Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.3
(4 Ratings)
5 star 50%
4 star 0%
3 star 0%
2 star 25%
1 star 25%
General Malfunction Jan 12, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have been developing large scale simulation models for many years now, mostly in Java. Recently I decided to check out Julia in this area. This book caught my attention, because it featured recipes directly from my field of interest such as Monte Carlo simulation or modeling queuing systems. The biggest benefit from reading it was understanding how to implement numerical computing routines efficiently in Julia (which is a pain in Java). Also the last chapter on distributed computing was very valuable, as I have found that with the functionalities shipped with the language I am was scale my simulations very easily. I think that the book is relatively well written, however, I would say that beginners might find it challenging, as it assumes that the reader has some basic knowledge of Julia and computing principles in general.
Amazon Verified review Amazon
Pawel Pralat Jan 12, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I am a research scientist (professor of mathematics) with past experience in using C/C++ (for heavy number crunching) and Maple (for everything else).After studying the recipes in the book, I now see that Julia is a great language that combines easy code (such as Maple or Python) and speed of C. For other researchers I would especially recommend Chapter 4: “Numerical Computing with Julia”, where one can learn how to write high-performance Julia code. Also Chapter 1: “Installing and Setting Up Julia” saved me a lot of time, as usually starting using a new computational environment requires to learn a lot of small details, which are nicely laid out in the book. Julia is famous for its ease to scale out the computations for many CPU cores and nodes. The recipes in the Chapter 10, "Distributed Computing", turned to be great companion to the existing Julia documentation. To book presents Julia high performance computing from a very practical perspective and made it much easier to scale out scientific computations.
Amazon Verified review Amazon
DrJimbo Dec 17, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
I was somewhat optimistic that this publication would be worthwhile insofar as one of the authors is a very frequent and helpful contributor to Julia support on StackOverflow. Regrettably this was not to be.For example, Chapter 1 provides a fairly thorough overview of Julia program installation along with various IDE's for Windows and Linux Ubuntu - iOS/mac systems are not even referenced! Now this is probably due to the authors residing in Poland where iOS/mac systems are less prevalent, but this publication is being promoted in the U.S. where iOS/mac systems are very prevalent especially in technical computing environments.Let's consider Chapter 2 - Data Structures and AlgorithmsConsider the first example - Finding the index of a random minimum element in an array. The authors launch right into the creation of functions which employ arrays without bothering to discuss the nature of function definitions or arrays. Moreover the second function in the section is relatively complex and involves Julia programming structures previously unmentioned, let alone discussed in detail until much later in the book. Later in Chapter #2 there is a far more complex set of functions to optimize matrix multiplication, perhaps valuable in numerical programming contexts but not necessarily a cookbook example of substantial urgency, especially so early in the volume.Chapter 5 - Variables, Types and Function. Put to the side for the moment that Chapters 2 through 4 have been making extensive use of these three aspects of Julia programming so this chapter arrives a bit late in the game. To begin the discussion of types Julia structures are used - would you like see where structures are thoroughly reviewed? Forget about looking at the Index in the back of the book - "Sudoku" is there, but no "struct". In short, you are left to understand the nature of structures while in the midst of a section on types and type hierarchy.While not altogether unworthy this volume betrays an all too frequent issue with Packt publications - no responsible editorial review. By way of comparison, O'Reilly technical publications, but particularly their "Cookbooks", are wonderfully organized and employ examples which are widely relevant and progress methodically from the simple to the more complex.Given the freely available Julia programing resources this volume is to my mind of tertiary value.
Amazon Verified review Amazon
Marcello Vitaletti Dec 24, 2019
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Many math expressions are unreadable in the Kindle edition
Amazon Verified review Amazon
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