Swift へ雑移植 http://qiita.com/ynakayama/items/59beb40b7c3829cc0bf2

recommender.swift

//: Playground - noun: a place where people can play

import UIKit

let dataset = [
    "山田": [
        "カレー": 2.5,
        "ラーメン": 3.5,
        "チャーハン": 3.0,
        "寿司": 3.5,
        "牛丼": 2.5,
        "うどん": 3.0,
    ],
    "田中": [
        "カレー": 3.0,
        "ラーメン": 3.5,
        "チャーハン": 1.5,
        "寿司": 5.0,
        "うどん": 3.0,
        "牛丼": 3.5,
    ],
    "佐藤": [
        "カレー": 2.5,
        "ラーメン": 3.0,
        "寿司": 3.5,
        "うどん": 4.0,
    ],
    "中村": [
        "ラーメン": 3.5,
        "チャーハン": 3.0,
        "うどん": 4.5,
        "寿司": 4.0,
        "牛丼": 2.5,
    ],
    "川村": [
        "カレー": 3.0,
        "ラーメン": 4.0,
        "チャーハン": 2.0,
        "寿司": 3.0,
        "うどん": 3.0,
        "牛丼": 2.0,
    ],
    "鈴木": [
        "カレー": 3.0,
        "ラーメン": 4.0,
        "うどん": 3.0,
        "寿司": 5.0,
        "牛丼": 3.5,
    ],
    "下林": [
        "ラーメン": 4.5,
        "牛丼": 1.0,
        "寿司": 4.0,
    ]
]

func calcSimilarityScore(person1: String, person2: String) -> Double {
    var bothViewed = [String: Int]()
    for item in dataset[person1]! {
        if dataset[person2]![item.0] != nil {
            bothViewed[item.0] = 1
        }
    }
    
    if bothViewed.count == 0 {
        return 0
    }
    
    var sumOfEclideanDistance: [Double] = []
    for item in dataset[person1]! {
        if dataset[person2]![item.0] != nil {
            sumOfEclideanDistance.append(
                pow(dataset[person1]![item.0]! - dataset[person2]![item.0]!, 2))
        }
    }
    
    let totalOfEclideanDistance = sumOfEclideanDistance.reduce(0, combine: +)
    return 1 / (1 + sqrt(totalOfEclideanDistance))
}

func calcPearsonCorrelation(person1: String, person2: String) -> Double {
    var bothRated = [String: Int]()
    for item in dataset[person1]! {
        if dataset[person2]![item.0] != nil {
            bothRated[item.0] = 1
        }
    }
    
    let numberOfRatings = Double(bothRated.count)
    if numberOfRatings == 0 {
        return 0
    }
    
    var person1PreferencesSum       = 0.0
    var person2PreferencesSum       = 0.0
    var person1SquarePreferencesSum = 0.0
    var person2SquarePreferencesSum = 0.0
    var productSumOfBothUsers       = 0.0
    for item in bothRated {
        person1PreferencesSum       += dataset[person1]![item.0]!
        person2PreferencesSum       += dataset[person2]![item.0]!
        person1SquarePreferencesSum += pow(dataset[person1]![item.0]!, 2)
        person2SquarePreferencesSum += pow(dataset[person2]![item.0]!, 2)
        productSumOfBothUsers       += dataset[person1]![item.0]! * dataset[person2]![item.0]!
    }
    
    let denominatorValue = sqrt((person1SquarePreferencesSum - pow(person1PreferencesSum, 2) / numberOfRatings) * (person2SquarePreferencesSum - pow(person2PreferencesSum, 2) / numberOfRatings))
    
    if denominatorValue == 0 {
        return 0
    }
    
    let numeratorValue = productSumOfBothUsers - (person1PreferencesSum * person2PreferencesSum / numberOfRatings)
    return numeratorValue / denominatorValue
}

func getMostSimilarUsers(person: String, numberOfUsers: Int) -> [(String, Double)] {
    var scores = [String: Double]()
    for item in dataset {
        if person != item.0 {
            scores[item.0] = calcPearsonCorrelation(person, person2: item.0)
        }
    }
    
    return Array(scores.sort{$0.1 > $1.1}[0..<numberOfUsers])
}

func getUserRecommendations(person: String) -> [String] {
    var totals  = [String: Double]()
    var simSums = [String: Double]()
    for other in dataset {
        if other.0 == person {
            continue
        }
        
        let sim = calcPearsonCorrelation(person, person2: other.0)
        if sim <= 0 {
            continue
        }
        
        for item in dataset[other.0]! {
            if dataset[person]![item.0] == nil || dataset[person]![item.0] == 0 {
                if totals[item.0] == nil {
                    totals[item.0] = 0
                }
                totals[item.0] = totals[item.0]! + item.1 * sim
                
                if simSums[item.0] == nil {
                    simSums[item.0] = 0
                }
                simSums[item.0] = simSums[item.0]! + sim
            }
        }
    }
    
    // 正規化されたリストを作成
    var rankings = [String: Double]()
    for item in totals {
        rankings[item.0] = item.1 / simSums[item.0]!
    }
    
    return rankings.sort{$0.1 > $1.1}.map{$0.0}
}


print("山田さんと鈴木さんの類似度 (ユークリッド距離)", calcSimilarityScore("山田", person2: "鈴木"))
print("山田さんと田中さんの類似度 (ピアソン相関係数)", calcPearsonCorrelation("山田", person2: "田中"))
dump(getMostSimilarUsers("山田", numberOfUsers: 3))
dump(getUserRecommendations("下林"))

result

山田さんと鈴木さんの類似度 (ユークリッド距離) 0.340542426583167
山田さんと田中さんの類似度 (ピアソン相関係数) 0.39605901719067
▿ 3 elements
  ▿ [0]: (2 elements)
    - .0: 下林
    - .1: 0.99124070716193
  ▿ [1]: (2 elements)
    - .0: 鈴木
    - .1: 0.747017880833996
  ▿ [2]: (2 elements)
    - .0: 川村
    - .1: 0.594088525786004
▿ 3 elements
  - [0]: うどん
  - [1]: カレー
  - [2]: チャーハン