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

Recognizing human motion using KNN

Core Motion is an iOS framework that provides an API for inertial sensors of mobile devices. It also recognizes some user motion types, and stores them to the HealthKit database.


If you are not familiar with Core Motion API, please check the framework reference: The code for this example can be found in the Code/02DistanceBased/ MotionClassification folder of supplementary materials.

As per iOS 11 beta 2, the CMMotionActivity class includes the following types of motion:

  • Stationary
  • Walking
  • Running
  • Automotive
  • Cycling

Everything else falls into an unknown category or is recognized as one of the preceding. Core Motion doesn't provide a way to recognize custom motion types so we'll train our own classifier for this purpose. Unlike decision trees from the previous chapter, KNN will be trained on device end-to-end. It will also not be frozen inside Core ML because as we keep all the control on it, we'll be able to...