diff -r 59eeb22c9229 -r fced9a61c881 assignment2021scala/main2/danube.scala
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/assignment2021scala/main2/danube.scala	Mon Nov 08 23:17:51 2021 +0000
@@ -0,0 +1,161 @@
+// Main Part 2 about Movie Recommendations 
+// at Danube.co.uk
+//===========================================
+
+object M2 {
+
+import io.Source
+import scala.util._
+
+// (1) Implement the function get_csv_url which takes an url-string
+//     as argument and requests the corresponding file. The two urls
+//     of interest are ratings_url and movies_url, which correspond 
+//     to CSV-files.
+//
+//     The function should ReTurn the CSV-file appropriately broken
+//     up into lines, and the first line should be dropped (that is without
+//     the header of the CSV-file). The result is a list of strings (lines
+//     in the file).
+
+def get_csv_url(url: String) : List[String] = ???
+
+
+val ratings_url = """https://nms.kcl.ac.uk/christian.urban/ratings.csv"""
+val movies_url = """https://nms.kcl.ac.uk/christian.urban/movies.csv"""
+
+// testcases
+//-----------
+//:
+//val movies = get_csv_url(movies_url)
+
+//ratings.length  // 87313
+//movies.length   // 9742
+
+
+
+// (2) Implement two functions that process the CSV-files from (1). The ratings
+//     function filters out all ratings below 4 and ReTurns a list of 
+//     (userID, movieID) pairs. The movies function just ReTurns a list 
+//     of (movieID, title) pairs. Note the input to these functions, that is
+//     the argument lines, will be the output of the function get_csv_url.
+
+
+def process_ratings(lines: List[String]) : List[(String, String)] = ???
+
+def process_movies(lines: List[String]) : List[(String, String)] = ???
+
+
+// testcases
+//-----------
+//val good_ratings = process_ratings(ratings)
+//val movie_names = process_movies(movies)
+
+//good_ratings.length   //48580
+//movie_names.length    // 9742
+
+
+
+
+// (3) Implement a grouping function that calculates a Map
+//     containing the userIDs and all the corresponding recommendations 
+//     (list of movieIDs). This  should be implemented in a tail
+//     recursive fashion, using a Map m as accumulator. This Map m
+//     is set to Map() at the beginning of the calculation.
+
+def groupById(ratings: List[(String, String)], 
+              m: Map[String, List[String]]) : Map[String, List[String]] = ???
+
+
+// testcases
+//-----------
+//val ratings_map = groupById(good_ratings, Map())
+//val movies_map = movie_names.toMap
+
+//ratings_map.get("414").get.map(movies_map.get(_)) 
+//    => most prolific recommender with 1227 positive ratings
+
+//ratings_map.get("474").get.map(movies_map.get(_)) 
+//    => second-most prolific recommender with 787 positive ratings
+
+//ratings_map.get("214").get.map(movies_map.get(_)) 
+//    => least prolific recommender with only 1 positive rating
+
+
+
+// (4) Implement a function that takes a ratings map and a movie_name as argument.
+//     The function calculates all suggestions containing
+//     the movie in its recommendations. It ReTurns a list of all these
+//     recommendations (each of them is a list and needs to have the movie deleted, 
+//     otherwise it might happen we recommend the same movie).
+
+
+def favourites(m: Map[String, List[String]], mov: String) : List[List[String]] = ???
+
+
+// testcases
+//-----------
+// movie ID "912" -> Casablanca (1942)
+//          "858" -> Godfather
+//          "260" -> Star Wars: Episode IV - A New Hope (1977)
+
+//favourites(ratings_map, "912").length  // => 80
+
+// That means there are 80 users that recommend the movie with ID 912.
+// Of these 80  users, 55 gave a good rating to movie 858 and
+// 52 a good rating to movies 260, 318, 593.
+
+
+
+// (5) Implement a suggestions function which takes a rating
+//     map and a movie_name as arguments. It calculates all the recommended
+//     movies sorted according to the most frequently suggested movie(s) first.
+
+def suggestions(recs: Map[String, List[String]], 
+                mov_name: String) : List[String] = ???
+
+
+// testcases
+//-----------
+
+//suggestions(ratings_map, "912")
+//suggestions(ratings_map, "912").length  
+// => 4110 suggestions with List(858, 260, 318, 593, ...)
+//    being the most frequently suggested movies
+
+
+
+// (6) Implement a recommendations function which generates at most
+//     *two* of the most frequently suggested movies. It ReTurns the 
+//     actual movie names, not the movieIDs.
+
+
+def recommendations(recs: Map[String, List[String]],
+                    movs: Map[String, String],
+                    mov_name: String) : List[String] = ???
+
+
+
+// testcases
+//-----------
+// recommendations(ratings_map, movies_map, "912")
+//   => List(Godfather, Star Wars: Episode IV - A NewHope (1977))
+
+//recommendations(ratings_map, movies_map, "260")
+//   => List(Star Wars: Episode V - The Empire Strikes Back (1980), 
+//           Star Wars: Episode VI - Return of the Jedi (1983))
+
+// recommendations(ratings_map, movies_map, "2")
+//   => List(Lion King, Jurassic Park (1993))
+
+// recommendations(ratings_map, movies_map, "0")
+//   => Nil
+
+// recommendations(ratings_map, movies_map, "1")
+//   => List(Shawshank Redemption, Forrest Gump (1994))
+
+// recommendations(ratings_map, movies_map, "4")
+//   => Nil  (there are three ratings for this movie in ratings.csv but they are not positive)     
+
+
+
+}