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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

Finding frequent item sets


The first step of the algorithm that we implement is based on the support measure. This function returns a set of all item sets with support larger than minSupport:

func frequentItemSets(minSupport: Double) -> Set<ItemSet> { 
     
    var itemSets = Set<ItemSet>() 
     
    let emptyItemSet: ItemSet = ItemSet() 
     
    supporters[emptyItemSet] = Array(0 ..< transactions.count) 

Here we use the priority queue data structure to keep track of possible extensions.

Note

There is no priority queue implementation in the Foundation or Swift standard libraries, and standard data structures are out of the scope of this book. We are using the open source implementation by David Kopec (MIT license): https://github.com/davecom/SwiftPriorityQueue.

To make it work with item sets we had to change the code a bit—instead of being parameterized with the comparable types, it is now parameterized with types conforming to the equatable protocol:

    var queue = PriorityQueue...
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