TestsTested | ✓ |
LangLanguage | SwiftSwift |
License | MIT |
ReleasedLast Release | Dec 2016 |
SwiftSwift Version | 3.0 |
SPMSupports SPM | ✗ |
Maintained by Enrique de la Torre.
This repository is DEPRECATED. Refer to HRLClassifier instead.
Use Machine Learning to predict if a person is working out based of his/her heart rate.
To run the example project, clone the repo, and run pod install
from the Example directory first.
HRLEngine is available through CocoaPods. To install it, simply add the following line to your Podfile:
pod "HRLEngine"
let date = Date()
let dataFrame = DataFrame()
dataFrame.append(record: Record(date:date.addingTimeInterval(-6 * 60 * 60),
bpm:Float(100)),
isWorkingOut: false)
dataFrame.append(record: Record(date:date.addingTimeInterval(-5 * 60 * 60),
bpm:Float(140)),
isWorkingOut: true)
dataFrame.append(record: Record(date:date.addingTimeInterval(-4 * 60 * 60),
bpm:Float(120)),
isWorkingOut: true)
dataFrame.append(record: Record(date:date.addingTimeInterval(-3 * 60 * 60),
bpm:Float(70)),
isWorkingOut: false)
dataFrame.append(record: Record(date:date.addingTimeInterval(-2 * 60 * 60),
bpm:Float(55)),
isWorkingOut: false)
dataFrame.append(record: Record(date:date.addingTimeInterval(-1 * 60 * 60),
bpm:Float(125)),
isWorkingOut: true)
let engine = Engine()
engine.train(with: dataFrame)
let predictDate = date.addingTimeInterval(-3.5 * 60 * 60)
let predictBPM = Float(130)
let isWorkingOut = engine.predictedWorkingOut(for: Record(date:predictDate,
bpm:predictBPM))
print("At \(predictDate) with \(predictBPM) bpm, is user working out? \(isWorkingOut)")
HRLEngine is available under the MIT license. See the LICENSE file for more info.