denpazakura

import Foundation

enum HTTPClientError {
    case badRequest
    case unauthorized
    case paymentRequired
    case forbidden
    case notFound
    case methodNotAllowed
    case notAcceptable

denpazakura

import Foundation

protocol Repository {
    associatedtype Item
    
    func getItems() -> [Item]
    func deleteItems()
}

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# Calculating median in Swift:

import Foundation

let sample = [0, 1, 5, 7, 10, 14]

func median<T: BinaryFloatingPoint & Comparable>(sample: Array<T>) -> T? {
    let orderedSample = sample.sorted()
    let count = orderedSample.count
    let length_is_even = orderedSample.count % 2 == 0

denpazakura

# Calculating modes in Swift

func modes<T: BinaryFloatingPoint & Hashable>(sample: Array<T>) -> Array<T> {
    let mapped = sample.map { ($0, 1) }
    let counts = Dictionary(mapped, uniquingKeysWith: +)
    
    let max_count = counts.values.max() ?? 0
    
    let modes = counts.filter { $0.value == max_count }.map { $0.key }
    

denpazakura

# Calculating variance in Swift

let data = [0, 1, 5, 7, 9, 10, 14]

func variance<T: BinaryFloatingPoint>(input: Array<T>) -> T {
    let sum = input.reduce(0, +)
    let mean = sum / T(input.count)
    
    let sumOfSquaredDifferences = input.reduce(0) { $0 + (mean - $1) * (mean - $1) }

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# The normal probability density function (PDF) is used in statistics to calculate the probability of a specific value occurring in a normal distribution. 

func normalProbabilityDensityFunction(x: Double, mean: Double, stdDev: Double) -> Double {
    let power = pow((x - mean), 2)
    let numerator = exp(-1.0 * power / (2.0 * stdDev * stdDev))
    let denominator = 2.0 * Double.pi * stdDev * stdDev
    return 1.0 / sqrt(denominator) * numerator
}

print(normalProbabilityDensityFunction(x: 60, mean: 64.43, stdDev: 2.99))

denpazakura

# Calculating cdf using erf (error function) provided by Darwin.C.math
# TODO: write an explanation in comments

func normalCumulativeDensityFunction(x: Double, mean: Double, stdDev: Double) -> Double {
    return 0.5 * (1.0 + erf((x - mean) / (stdDev * sqrt(2.0))))
}

print(normalCumulativeDensityFunction(x: 64.43, mean: 64.43, stdDev: 2.99))

denpazakura

let s2pi = sqrt(2.0 * .pi)
let P0 = [ -1.26551223, 1.00002368, 0.37409196, 0.09678418, -0.18628806, 0.27886807, -1.13520398, 1.48851587, -0.82215223, 0.17087277 ]
let Q0 = [ -0.15172908, 1.84759012, -3.68455089, 3.31335053, -1.01539585, 0.13295073, -0.02065141, -0.00900744, 0.00711589, 0.00392377 ]
let P1 = [ -0.73700162, 1.17160096, 0.63439328, 0.29963877, 0.21951691, 0.23392674, 0.31539329, 0.72463255 ]
let Q1 = [ -0.12712856, 1.99197325, -5.63224284, 7.49868308, -5.18611478, 0.55327544, 1.10514223, 0.33659149 ]
let P2 = [ -0.80082143, 1.81891395, 1.84631832, 1.19352598, 0.91109542, 0.06219856, -0.76820489, 0.56827526 ]
let Q2 = [ -0.10846517, 2.11237046, -6.72875739, 8.09336846, -5.09373263, 1.15688468, 3.89290431, 1.82524606 ]

func polevl(_ x: Double, _ coef: [Double]) -> Double {
    var ans = coef[0]

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func plusMinus(input: [Int]) {
        let weight = 1 / Double(input.count)
        
        let positives = input.filter( { $0 > 0 } )
        let negatives = input.filter( { $0 < 0 } )
        let zeroes = input.filter( { $0 == 0 } )
        
        let positiveRat = Double(positives.count) * weight
        let negativeRat = Double(negatives.count) * weight
        let zeroRat = Double(zeroes.count) * weight

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func minMaxSum(input: [Int]) {
        let sum = input.reduce(0, +)
        
        let min = input.min() ?? 0
        let max = input.max() ?? 0
        
        let minSum = sum - max
        let maxSum = sum - min
        
        print(minSum, maxSum)