Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev

Overview of this book

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Table of Contents (18 chapters)
Title Page
Packt Upsell

Defining data structures

What we want to have by the end of this chapter is a rule learning algorithm called Apriori. We will learn about the algorithm details later; for now, we only want to define the data structures that we will work with throughout the chapter, along with some utility functions.

The generic structure for the algorithm is as follows:

public struct Apriori<Item: Hashable & Equatable> { 

In the simplest case, the ordering of the items in the transaction doesn't matter, and neither does their number nor the associated timestamps. This means that we consider our item sets and transactions as mathematical or Swift sets:

public typealias ItemSet = Set<Item> 

The parameter I is a type of item in your transactions. Next, we have to implement some structures for subsets and rules:

class Subsets: Sequence {
  var subsets: [ItemSet]
  init(_ set: ItemSet) {
    self.subsets = Array(set).combinations().map(Set.init)
  func makeIterator() -> AnyIterator<ItemSet...