diff -r b5b6ed38c2f2 -r 682611a0fb89 main_solution2/danube.scala
--- a/main_solution2/danube.scala	Mon Nov 02 13:10:02 2020 +0000
+++ b/main_solution2/danube.scala	Wed Nov 04 14:46:03 2020 +0000
@@ -160,20 +160,37 @@
 //   => List(Shawshank Redemption, Forrest Gump (1994))
 
 // recommendations(ratings_map, movies_map, "4")
-//   => Nil  (there are three ratings fro this movie in ratings.csv but they are not positive)     
+//   => 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
+}
 
 
-// If you want to calculate the recomendations for all movies.
-// Will take a few seconds calculation time.
+//most_recommended(ratings_map, movies_map).take(3)
+// =>
+// List((Matrix,698), 
+//      (Star Wars: Episode IV - A New Hope (1977),402), 
+//      (Jerry Maguire (1996),382))
 
-//val all = for (name <- movie_names.map(_._1)) yield {
-//  recommendations(ratings_map, movies_map, name)
-//}
-
-// helper functions
-//List().take(2
-//List(1).take(2)
-//List(1,2).take(2)
-//List(1,2,3).take(2)
 
 }