--- a/main_templates2/danube.scala Fri Apr 26 17:29:30 2024 +0100
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,161 +0,0 @@
-// 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)
-
-
-
-}