diff -r 017f621f5835 -r 3ffe978a5664 cws/main_cw02.tex
--- a/cws/main_cw02.tex	Thu Nov 04 12:20:12 2021 +0000
+++ b/cws/main_cw02.tex	Fri Nov 05 16:47:55 2021 +0000
@@ -9,7 +9,7 @@
 
 %% should ask to lower case the words.
 
-\section*{Main Part 2 (Scala, 7 Marks)}
+\section*{Main Part 2 (Scala, 6 Marks)}
 
 
 \noindent
@@ -39,7 +39,7 @@
 the command line with the option \texttt{-cp danube.jar} and then
 query any function from the template file. Say you want to find out
 what the function \texttt{} produces: for this you just need
-to prefix it with the object name \texttt{CW7b}.  If you want to find out what
+to prefix it with the object name \texttt{M2}.  If you want to find out what
 these functions produce for the list \texttt{List("a", "b", "b")},
 you would type something like:
 
@@ -48,7 +48,7 @@
 scala> val ratings_url =
      | """https://nms.kcl.ac.uk/christian.urban/ratings.csv"""
 
-scala> CW7b.get_csv_url(ratings_url)
+scala> M2.get_csv_url(ratings_url)
 val res0: List[String] = List(1,1,4 ...)
 \end{lstlisting}%$
 
@@ -70,7 +70,7 @@
 \newpage
 
 
-\subsection*{Main Part 2 (7 Marks, file danube.scala)}
+\subsection*{Main Part 2 (6 Marks, file danube.scala)}
 
 You are creating Danube.co.uk which you hope will be the next big thing
 in online movie renting. You know that you can save money by
@@ -189,17 +189,17 @@
   then return fewer than two movie names.\\
   \mbox{}\hfill [1 Mark]
 
-\item[(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 the movie was recommended. 
- Sort all the pairs according to the number
- of times they were recommended (most recommended movie name 
- first).\\
- \mbox{}\hfill [1 Mark]
+%\item[(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 the movie was recommended. 
+% Sort all the pairs according to the number
+% of times they were recommended (most recommended movie name 
+% first).\\
+% \mbox{}\hfill [1 Mark]
   
 \end{itemize}