Book Image

Julia Programming Projects

By : Adrian Salceanu
Book Image

Julia Programming Projects

By: Adrian Salceanu

Overview of this book

Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia.

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Table of Contents (19 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Dedication
Dedication
About Packt
About Packt
Contributors
Contributors
Preface
Preface
Free Chapter
1
Getting Started with Julia Programming
Getting Started with Julia Programming
Technical requirements
Why Julia?
Installing Julia
Choosing an IDE
Getting started with Julia
The package system
Summary
2
Creating Our First Julia App
Creating Our First Julia App
Technical requirements
Defining variables
Comments
Strings
Regular expressions
Raw string literals
Numbers
Tuples
Ranges
Arrays
Exploratory data analysis with Julia
Using simple statistics to better understand our data
Loading and saving our data
Summary
3
Setting Up the Wiki Game
Setting Up the Wiki Game
Technical requirements
Data harvesting through web scraping
Understanding HTML documents
Accessing the internet from Julia
Handling HTTP responses
Understanding HTTP responses
Dictionaries
Using the HTTP response
Coding defensively
Handling errors like a pro
Learning about functions
Writing a basic web crawler – take one
Carrying on with the crawler's implementation
Summary
4
Building the Wiki Game Web Crawler
Building the Wiki Game Web Crawler
Technical requirements
Six Degrees of Wikipedia, the gameplay
Organizing our code
Setting up our game's architecture
Building our Wikipedia crawler - take two
Implementing the gameplay
Finishing touches
Learning about Julia's type system
Using article types
Methods
Working with relational databases
Summary
5
Adding a Web UI for the Wiki Game
Adding a Web UI for the Wiki Game
Technical requirements
The game plan
Learning about Julia's web stack
Developing the game's web UI
Starting a new game
Displaying a Wikipedia article page
Showing the solution
Handling any other requests
Wrapping it up
Summary
6
Implementing Recommender Systems with Julia
Implementing Recommender Systems with Julia
Technical requirements
Understanding recommender systems
Classifying recommender systems
Summary
7
Machine Learning for Recommender Systems
Machine Learning for Recommender Systems
Technical requirements
Comparing the memory-based versus model-based recommenders
Machine learning-based recommendations
Testing the recommendations
Learning about hybrid recommender systems
Summary
8
Leveraging Unsupervised Learning Techniques
Leveraging Unsupervised Learning Techniques
Technical requirements
Unsupervised machine learning
Clustering
Data analysis of the San Francisco business
Unsupervised machine learning with clustering
Summary
9
Working with Dates, Times, and Time Series
Working with Dates, Times, and Time Series
Technical requirements
Working with dates and times
Constructing dates and times
Working with date and time accessors
Defining the date ranges
Period types and period arithmetic
Date adjustments
Rounding of dates
Adding support for time zones
Time series data in Julia
Summary
10
Time Series Forecasting
Time Series Forecasting
Technical requirements
A quick look at our data
Understanding time series components
Time series decomposition
Time series stationarity
Time series forecasting
Summary
11
Creating Julia Packages
Creating Julia Packages
Technical requirements
Creating a new Julia package
Defining the requirements for our package
Beginning with test-driven Julia development
Performance testing
Interacting with the GitHub API
Documenting our package
Advanced documentation tips
Registering our package
Summary
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think
Index
Index

Developing the game's web UI

Please start your favorite Julia editor and open the sixdegrees/ folder we used in the previous chapter. It should contain all the files that we've worked on already—six_degrees.jl, plus the Articles, Database, Gameplay, and Wikipedia modules.

Note

If you haven't followed through the code up to this point, you can download this chapter's accompanying support files, which are available at https://github.com/PacktPublishing/Julia-Programming-Projects/tree/master/Chapter05.

Add a new file for our web app. Since the code will be more complex this time and should integrate with the rest of our modules, let's define a WebApp module within a new WebApp.jl file. Then, we can add these first few lines of code:

module WebApp 
 
using HTTP, Sockets 
 
const HOST = ip"0.0.0.0" 
const PORT = 8888 
const ROUTER = HTTP.Router() 
const SERVER = HTTP.Server(ROUTER) 
 
HTTP.serve(SERVER, HOST, PORT) 
 
end

No surprises here—similar to the previous example, we define constants for...

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