A simple, naive implementation of gradient descent/linear regression in Swift using capacity loss vs mileage of Nissan Leaf

SimpleLinearRegression.swift

import UIKit

var milesDriven:[Double] = [0,100,200,300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500,1600,1700,1800,12000,16000,20000,13633,25000,11383,10200,15700,12734,14278,24000,25500,10052,21085,21451,21000,20300,20100,16000,17500,15000,20490,34173,31510,22900,23800,19000,10500,14000,19200,16500,22700,18300,19345,20791,16500,15868,13884,11275,10300,14180,23652,14900,16800,13365,32500,20651,27177,31473,15472,27435,26797,50600,41972,25725,15900,23000,44733,33700,35989,21649,34500,73205,53171,41000,30000,44263,54661,44076,39700,44897,34900,26495,28190,43520,28700,53600,55475,58078,40755,39801,34000,86400,52600,129300,61150,59983,48168,50952,55800,56000]

var barsLost:[Double] = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4]

var minMileage:Double = milesDriven.min()!
var maxMileage:Double = milesDriven.max()!
var milesDrivenScaled = milesDriven.map { ($0 - minMileage) / maxMileage - minMileage } //normalize the miles

var minBars:Double = barsLost.min()!
var maxBars:Double = barsLost.max()!
var barsLostScaled = barsLost.map { ($0 - minBars) / maxBars - minBars }                //normalize the bar loss

//Play with these values
var iterations:Int = 500
var learningRate:Double = 0.01
var predictionMiles:Double = 23000

var scaledPredictionMiles:Double = (predictionMiles - minMileage) / maxMileage - minMileage
var JHistory = [Double?](repeating: nil, count: iterations)  //To get the charted output in the playground

func predictedBarsLost(intercept:Double, slope:Double, miles:Double) -> Double {
    return intercept + slope * miles
}

func findBestFit(x:[Double], y:[Double], iterations:Int, alpha:Double) -> (Double, Double) {
    var intercept:Double = 0.0
    var slope:Double = 0.0
    
    let numberOfSamples = x.count - 1
    
    var J:Double!
    
    for n in 1...iterations {
        for i in 0...numberOfSamples {
            J = y[i] - predictedBarsLost(intercept: intercept, slope: slope, miles: x[i])
            intercept += alpha * J
            slope += alpha * J * x[i]
        }
        
        JHistory[n-1] = J                   //watch this array to make sure it's decreasing
    }
    
    return (intercept, slope)
}

var (best_Intercept, best_Slope) = findBestFit(x: milesDrivenScaled, y: barsLostScaled, iterations: iterations, alpha: learningRate)
var predictedBarLoss = predictedBarsLost(intercept: best_Intercept, slope: best_Slope, miles: (scaledPredictionMiles))

//calculate the mileage and bar loss from the scaled values
print("At \((scaledPredictionMiles + minMileage) * maxMileage) miles we predict a loss of \((predictedBarLoss + minBars) * maxBars) bars.")