happy_classroom's Introduction

Happy Classroom

Calculate optimal groupings of a target population into a set of subsets of equal/near-equal size. Exhaustively searches all possible subset combinations while avoiding duplicate search branches. I.e. having searched subset [1, 2], algorithm will skip [2,1]; and having searched result set [[1,2], [3,4]], algorithm will skip [[3,4], [1,2]].

Written for the SWI Prolog environment.

Example

?- classroom([p(1), p(2), p(3), p(4), p(5), p(6)], 2, Classroom).
Classroom = groups([[p(1), p(2)], [p(3), p(4)], [p(5), p(6)]], 21) ;
Classroom = groups([[p(1), p(2)], [p(3), p(5)], [p(4), p(6)]], 21) ;
Classroom = groups([[p(1), p(2)], [p(3), p(6)], [p(4), p(5)]], 21) ;
Classroom = groups([[p(1), p(3)], [p(2), p(4)], [p(5), p(6)]], 21) ;
Classroom = groups([[p(1), p(3)], [p(2), p(5)], [p(4), p(6)]], 21) ;
Classroom = groups([[p(1), p(3)], [p(2), p(6)], [p(4), p(5)]], 21) ;
Classroom = groups([[p(1), p(4)], [p(2), p(3)], [p(5), p(6)]], 21) ;
Classroom = groups([[p(1), p(4)], [p(2), p(5)], [p(3), p(6)]], 21) ;
Classroom = groups([[p(1), p(4)], [p(2), p(6)], [p(3), p(5)]], 21) ;
Classroom = groups([[p(1), p(5)], [p(2), p(3)], [p(4), p(6)]], 21) ;
Classroom = groups([[p(1), p(5)], [p(2), p(4)], [p(3), p(6)]], 21) ;
Classroom = groups([[p(1), p(5)], [p(2), p(6)], [p(3), p(4)]], 21) ;
Classroom = groups([[p(1), p(6)], [p(2), p(3)], [p(4), p(5)]], 21) ;
Classroom = groups([[p(1), p(6)], [p(2), p(4)], [p(3), p(5)]], 21) ;
Classroom = groups([[p(1), p(6)], [p(2), p(5)], [p(3), p(4)]], 21) ;
false.

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