import HRLClassifier

let filename = "/path/to/archive"

let baseDate = Date(timeIntervalSinceReferenceDate: 0)
let dayInterval = 24 * 60 * 60
let maxBPM = 200

// Fill data frame
var auxDataFrame = NSKeyedUnarchiver.unarchiveObject(withFile: filename) as? DataFrame
if auxDataFrame == nil {
    auxDataFrame = DataFrame()

    for i in 0..<7 {
        for _ in 0..<80 {
            let timeInterval = i * dayInterval + Int(arc4random_uniform(UInt32(dayInterval)))

            let date = baseDate.addingTimeInterval(TimeInterval(timeInterval))
            let bpm = Float(arc4random_uniform(UInt32(maxBPM)))
            let record = Record(date: date, bpm: bpm)

            let isWorkingOut = arc4random_uniform(2) == 1 ? true : false

            auxDataFrame!.append(record: record, isWorkingOut: isWorkingOut)
        }
    }
}

let dataFrame = auxDataFrame!

// Make classifier
guard let classifier = try? ClassifierFactory().makeClassifier(dataFrame: dataFrame) else {
    print("Given that all records are created in a random fashion, this is expected")

    return
}

// Archive data frame
NSKeyedArchiver.archiveRootObject(dataFrame, toFile: filename)

// Predict
let date = baseDate.addingTimeInterval(TimeInterval(7 * dayInterval))
let bpm = Float(arc4random_uniform(UInt32(maxBPM)))
let record = Record(date: date, bpm: bpm)

let prediction = classifier.predictedWorkingOut(for: record)

print("At \(date) with \(bpm) bpm, is user working out? \(prediction)")