--- a/main_solution2/danube.scala Sat Nov 06 00:06:39 2021 +0000
+++ b/main_solution2/danube.scala Mon Nov 08 00:17:50 2021 +0000
@@ -3,7 +3,7 @@
//========================================
-object CW7b { // for purposes of generating a jar
+object M2 { // for purposes of generating a jar
import io.Source
import scala.util._
@@ -57,11 +57,6 @@
//good_ratings.length //48580
//movie_names.length // 9742
-//==============================================
-// Do not change anything below, unless you want
-// to submit the file for the advanced part 3!
-//==============================================
-
// (3) Implement a grouping function that calulates a map
// containing the userIds and all the corresponding recommendations
@@ -162,35 +157,5 @@
// recommendations(ratings_map, movies_map, "4")
// => Nil (there are three ratings for this movie in ratings.csv but they are not positive)
-// (7) Calculate the recommendations for all movies according to
-// what the recommendations function in (6) produces (this
-// can take a few seconds). Put all recommendations into a list
-// (of strings) and count how often the strings occur in
-// this list. This produces a list of string-int pairs,
-// where the first component is the movie name and the second
-// is the number of how many times they were recommended.
-// Sort all the pairs according to the number
-// of times they were recommended (most recommended movie name
-// first).
-
-def occurrences(xs: List[String]): List[(String, Int)] =
- for (x <- xs.distinct) yield (x, xs.count(_ == x))
-
-def most_recommended(recs: Map[String, List[String]],
- movs: Map[String, String]) : List[(String, Int)] = {
- val all = (for (name <- movs.toList.map(_._1)) yield {
- recommendations(recs, movs, name)
- }).flatten
- val occs = occurrences(all)
- occs.sortBy(_._2).reverse
-}
-
-
-//most_recommended(ratings_map, movies_map).take(3)
-// =>
-// List((Matrix,698),
-// (Star Wars: Episode IV - A New Hope (1977),402),
-// (Jerry Maguire (1996),382))
-
}