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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

Clustering objects on a map

Where can we apply k-means in the context of mobile development? Clustering pins on a map may look like the most natural idea. Having the clusters of user locations, you can guess the location of the user's important locations like home and workplace, for example. We will implement pin clustering to visualize k-means, some of its unfortunate properties, and show why such an application of it may be not the best idea.

You can find a demo application under the 4_kmeans/MapKMeans folder of supplementary code. Everything interesting happens in the ViewController.swift. Clustering happens in the clusterize() method:

func clusterize() { 
  let k = Settings.k 
  colors = (0..<k).map{_ in Random.Uniform.randomColor()} 
  let data = savedAnnotations.map{ [Double]($0.coordinate) } 
  var kMeans = KMeans(k: k) 
  clusters = kMeans.train(data: data) 
 ...
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