Scala translation (Collective Intelligence)

recommendations.scala

val critics = Map(
  "Lisa Rose" -> Map(
    "Lady in the Water" -> 2.5,
    "Snakes on a Plane" -> 3.5,
    "Just My Luck" -> 3.0,
    "Superman Returns" -> 3.5,
    "You,Me and Dupree" -> 2.5,
    "The Night Listener" -> 3.0),
  "Gene Seymour" -> Map(
    "Lady in the Water" -> 3.0,
    "Snakes on a Plane" -> 3.5,
    "Just My Luck" -> 1.5,
    "Superman Returns" -> 5.0,
    "The Night Listener" -> 3.0,
    "You,Me and Dupree" -> 3.5),
  "Michael Phillips" -> Map( 
    "Lady in the Water" -> 2.5,
    "Snakes on a Plane" -> 3.0,
    "Superman Returns" -> 3.5,
    "The Night Listener" -> 4.0),
  "Claudia Puig" -> Map(
    "Snakes on a Plane" -> 3.5,
    "Just My Luck" -> 3.0,
    "Superman Returns" ->4.0,
    "The Night Listener" -> 4.5,
    "You,Me and Dupree" -> 2.5),
  "Mick LaSalle" -> Map( 
    "Lady in the Water" -> 3.0,
    "Snakes on a Plane" -> 4.0,
    "Just My Luck" -> 2.0,
    "Superman Returns" -> 3.0,
    "The Night Listener" -> 3.0,
    "You,Me and Dupree" -> 2.0),
  "Jack Matthews" -> Map( 
    "Lady in the Water" -> 3.0,
    "Snakes on a Plane" -> 4.0,
    "Superman Returns" -> 3.0,
    "The Night Listener" -> 3.0,
    "You,Me and Dupree" -> 2.0),
  "Toby" -> Map( 
    "Snakes on a Plane" -> 4.5,
    "You,Me and Dupree" -> 1.0,
    "Superman Returns" -> 4.0) )

import scala.math.{sqrt,pow}

def simDistance(prefs: Map[String,Map[String,Double]],person1: String,person2: String): Double = {
  // Get the set of shared items
  val si = (prefs(person1).keys.toSet intersect prefs(person2).keys.toSet).toList
  // Add up the squares of all the differences
  val sumOfSquares =  si.map(item => pow(prefs(person1)(item)-prefs(person2)(item),2)).sum
  // If they have no ratings in common,return 0
  1/(1+sqrt(sumOfSquares))
}

def simPearson(prefs: Map[String,Map[String,Double]],person1: String,person2: String): Double = {
  // Get the list of shared items
  val si = (prefs(person1).keys.toSet intersect prefs(person2).keys.toSet).toList
  val n = si.length
  // Add up all the preferences
  val sum1 =  si.map(item => prefs(person1)(item)).sum
  val sum2 =  si.map(item => prefs(person2)(item)).sum
  // Add up the squares 
  val sum1Sq =  si.map(item => pow(prefs(person1)(item),2)).sum
  val sum2Sq  =  si.map(item => pow(prefs(person2)(item),2)).sum
  // Sum up the products
  val pSum = si.map(item => prefs(person1)(item)*prefs(person2)(item)).sum
  // Calculate the Pearson score
  val num = pSum - (sum1*sum2/n)
  val den = sqrt((sum1Sq-pow(sum1,2)/n) * (sum2Sq-pow(sum2,2)/n))
  if (den != 0) num/den else 0
}

def topMatches(prefs: Map[String,Map[String,Double]],person: String,n:Int=5,similarity: (Map[String, Map[String,Double]], String, String) => Double =simPearson) = {
  val scores = prefs.filter((other) => other._1 != person).map { (other) =>
        (similarity(prefs,person,other._1),other._1) 
  }.toList
  scores.sortBy(_._1).reverse slice(0,n)
}