Let's say we have a Set S
which contains a few subsets:
- [a,b,c]
- [a,b]
- [c]
- [d,e,f]
- [d,f]
- [e]
Let's also say that S contains six unique elements: a, b, c, d, e
and f
.
How can we find all possible subsets of S
that contain each of the unique elements of S
exactly once?
The result of the function/method should be something like that:
[[a,b,c], [d,e,f]];
[[a,b,c], [d,f], [e]];
[[a,b], [c], [d,e,f]];
[[a,b], [c], [d,f], [e]].
Is there any best practice or any standard way to achieve that?
I would be grateful for a Pseudo-code, Ruby or Erlang example.
It sounds like what you are looking for are the partitions of a set/array.
One way of doing this is recursively:
- a 1 element array [x] has exactly one partition
- if x is an array of the form x = [head] + tail, then the partitions of x are generated by taking each partition of tail and adding head to each possible. For example if we were generating the partitions of [3,2,1] then from the partition [[2,1]] of [2,1] we can either add 3 to to [2,1] or as a separate element, which gives us 2 partitions [[3,2,1] or [[2,1], [3]] of the 5 that [1,2,3] has
A ruby implementation looks a little like
def partitions(x)
if x.length == 1
[[x]]
else
head, tail = x[0], x[1, x.length-1]
partitions(tail).inject([]) do |result, tail_partition|
result + partitions_by_adding_element(tail_partition, head)
end
end
end
def partitions_by_adding_element(partition, element)
(0..partition.length).collect do |index_to_add_at|
new_partition = partition.dup
new_partition[index_to_add_at] = (new_partition[index_to_add_at] || []) + [element]
new_partition
end
end
Why not to use the greedy algorithm?
1) sort set S descending using the subsets length
2) let i := 0
3) add S[i] to solution
4) find S[j] where j > i such as it contains of elements which are not in current solution
5) if you can't find element described in 4 then
5.a) clear solution
5.b) increase i
5.c) if i > |S| then break, no solution found ;(
5.d) goto 3
EDIT
Hmm, read again your post and come to conclusion that you need a Best-First search. Your question is not actually a partition problem because what you need is also known as Change-making problem but in the latter situation the very first solution is taken as the best one - you actually want to find all solutions and that's the reason why you should you the best-first search strategy approach.
It seems like a classic "backtrack" excercise.
- #1: Order your sets amongst eacother, so the backtrack will not give solutions twice.
- #2: current_set = [], set_list=[]
- #3: Loop Run through all the set that have lower order mark than the last in the set_list, (or all if the set_list is empty). Let call it set_at_hand
- #4: If set_at_hand has no intersection with current_set
- #4/true/1: Union it to current_set, also add to set_list.
- #4/true/2: current_set complete? true: add set_list to the list of correct solutions. false: recurse to #3
- #4/true/3: remove set_at_hand from current_set and set_list
- #5: End of loop
- #6: return
generate all subsets
def allSubsets set
combs=2**set.length
subsets=[]
for i in (0..combs) do
subset=[]
0.upto(set.length-1){|j| subset<<set[j] if i&(1<<j)!=0}
subsets<<subset
end
subsets
end
take a look here: https://github.com/sagivo/algorithms/blob/master/powerset.rb
this is a simple algorithm i built to find a powerset of an array.