Swift Array extension for standard deviation

Swift 4 Array extension with FloatingPoint elements:

extension Array where Element: FloatingPoint {

    func sum() -> Element {
        return self.reduce(0, +)
    }

    func avg() -> Element {
        return self.sum() / Element(self.count)
    }

    func std() -> Element {
        let mean = self.avg()
        let v = self.reduce(0, { $0 + ($1-mean)*($1-mean) })
        return sqrt(v / (Element(self.count) - 1))
    }

}

There's actually a class that provides this functionality already - called NSExpression. You could reduce your code size and complexity by using this instead. There's quite a bit of stuff to this class, but a simple implementation of what you want is as follows.

let expression = NSExpression(forFunction: "stddev:", arguments: [NSExpression(forConstantValue: [1,2,3,4,5])])
let standardDeviation = expression.expressionValueWithObject(nil, context: nil)

You can calculate mean too, and much more. Info here: http://nshipster.com/nsexpression/

To follow up on Matt's observation, I'd do the main algorithm on FloatingPoint, taking care of Double, Float, CGFloat, etc. But then I then do another permutation of this on BinaryInteger, to take care of all of the integer types.

E.g. on FloatingPoint:

extension Array where Element: FloatingPoint {
    
    /// The mean average of the items in the collection.
    
    var mean: Element { return reduce(Element(0), +) / Element(count) }
    
    /// The unbiased sample standard deviation. Is `nil` if there are insufficient number of items in the collection.
    
    var stdev: Element? {
        guard count > 1 else { return nil }
        
        return sqrt(sumSquaredDeviations() / Element(count - 1))
    }
    
    /// The population standard deviation. Is `nil` if there are insufficient number of items in the collection.
    
    var stdevp: Element? {
        guard count > 0 else { return nil }
        
        return sqrt(sumSquaredDeviations() / Element(count))
    }
    
    /// Calculate the sum of the squares of the differences of the values from the mean
    ///
    /// A calculation common for both sample and population standard deviations.
    ///
    /// - calculate mean
    /// - calculate deviation of each value from that mean
    /// - square that
    /// - sum all of those squares
    
    private func sumSquaredDeviations() -> Element {
        let average = mean
        return map {
            let difference = $0 - average
            return difference * difference
        }.reduce(Element(0), +)
    }
}

But then on BinaryInteger:

extension Array where Element: BinaryInteger {
    var mean: Double { return map { Double(exactly: $0)! }.mean }
    var stdev: Double? { return map { Double(exactly: $0)! }.stdev }
    var stdevp: Double? { return map { Double(exactly: $0)! }.stdevp }
}

Note, in my scenario, even when dealing with integer input data, I generally want floating point mean and standard deviations, so I arbitrarily chose Double. And you might want to do safer unwrapping of Double(exactly:). You can handle this scenario any way you want. But it illustrates the idea.

Not that I know Swift, but from numerics POV you're doing it a bit inefficiently

Basically, you're doing two passes (actually, three) over the array to compute two values, where one pass should be enough. Vairance might be expressed as E(X²) - E(X)², so in some pseudo-code:

tuple<float,float> get_mean_sd(data) {
    float s  = 0.0f;
    float s2 = 0.0f;
    for(float v: data) {
        s  += v;
        s2 += v*v;
    }
    s  /= count;
    s2 /= count;

    s2 -= s*s;
    return tuple(s, sqrt(s2 > 0.0 ? s2 : 0.0));
}

Just a heads-up, but when I tested the code outlined by Severin Pappadeux the result was a "population standard deviation" rather than a "sample standard deviation". You would use the first in an instance where 100% of the relevant data is available to you, such as when you are computing the variance around an average grade for all 20 students in a class. You would use the second if you did not have universal access to all the relevant data, and had to estimate the variance from a much smaller sample, such as estimating the height of all males within a large country.

The population standard deviation is often denoted as StDevP. The Swift 5.0 code I used is shown below. Note that this is not suitable for very large arrays due to loss of the "small value" bits as the summations get large. Especially when the variance is close to zero you might run into run-times errors. For such serious work you might have to introduce an algorithm called compensated summation

import Foundation

extension Array where Element: FloatingPoint
{

    var sum: Element {
        return self.reduce( 0, + )
    }
    
    var average: Element {
        return self.sum / Element( count )
    }
    
    /**
     (for a floating point array) returns a tuple containing the average and the "standard deviation for populations"
     */
    var averageAndStandardDeviationP: ( average: Element, stDevP: Element ) {
        
        let sumsTuple = sumAndSumSquared
        
        let populationSize = Element( count )
        let average = sumsTuple.sum / populationSize
        
        let expectedXSquared = sumsTuple.sumSquared / populationSize
        let variance = expectedXSquared - (average * average )
        
        return ( average, sqrt( variance ) )
    }
    
    /**
     (for a floating point array) returns a tuple containing the sum of all the values and the sum of all the values-squared
     */
    private var sumAndSumSquared: ( sum: Element, sumSquared: Element ) {
        return self.reduce( (Element(0), Element(0) ) )
        {
            ( arg0, x) in
            let (sumOfX, sumOfSquaredX) = arg0
            return ( sumOfX + x, sumOfSquaredX + ( x * x ) )
        }
    }
}