Add average to FloatingPoint arrays

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Sure, no problem. It’s pretty bare-bones:

struct Statistics<T: FloatingPoint> {
    private var mean  : T = 0
    private var ssqDev: T = 0
    
    private(set) var count: T = 0
    private(set) var min  : T = +.infinity
    private(set) var max  : T = -.infinity
    
    var average : T { return (count > 0) ? mean : .nan }
    var variance: T { return (count > 1) ? ssqDev / (count - 1) : .nan }
    var standardDeviation: T { return variance.squareRoot() }
    
    init() {}
    init<S: Sequence>(_ values: S) where S.Element == T {
        addValues(values)
    }
    
    mutating func addValues<S: Sequence>(_ values: S) where S.Element == T {
        for x in values { addValue(x) }
    }
    
    mutating func addValue(_ value: T) {
        count += 1
        min = Swift.min(min, value)
        max = Swift.max(max, value)
        
        let diff = value - mean
        let frac = diff / count
        mean    += frac
        ssqDev  += diff * (diff - frac)
    }
}

For my use-cases, I needed unbiased sample variance, hence Bessel’s correction. Also, there exists an error-compensating version of the “addValue” algorithm, but I didn’t need it so I went with the simple approach.

Tangentially, notice that I had to write “Swift.min()”, because of SR–2450.

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