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Machine Learning with Swift

You're reading from   Machine Learning with Swift Artificial Intelligence for iOS

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787121515
Length 378 pages
Edition 1st Edition
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Authors (3):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Oleksandr Baiev Oleksandr Baiev
Author Profile Icon Oleksandr Baiev
Oleksandr Baiev
Alexander Sosnovshchenko Alexander Sosnovshchenko
Author Profile Icon Alexander Sosnovshchenko
Alexander Sosnovshchenko
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Toc

Table of Contents (14) Chapters Close

Preface 1. Getting Started with Machine Learning FREE CHAPTER 2. Classification – Decision Tree Learning 3. K-Nearest Neighbors Classifier 4. K-Means Clustering 5. Association Rule Learning 6. Linear Regression and Gradient Descent 7. Linear Classifier and Logistic Regression 8. Neural Networks 9. Convolutional Neural Networks 10. Natural Language Processing 11. Machine Learning Libraries 12. Optimizing Neural Networks for Mobile Devices 13. Best Practices

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...
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