I have a sort of a one level tree structure as:
Where p are parent nodes, c are child nodes and b are hypothetical branches.
I want to find all combinations of branches under the constraint that only one parent can branch to only one child node, and two branches can not share parent and/or child.
E.g. if combo
is the set of combinations:
combo[0] = [b[0], b[3]]
combo[1] = [b[0], b[4]]
combo[2] = [b[1], b[4]]
combo[3] = [b[2], b[3]]
I think that's all of them. =)
How can this be achived automaticly in Python for arbitrary trees of this structures i.e. the number of p:s, c:s and b:s are arbitrary.
EDIT:
It is not a tree but rather a bipartite directed acyclic graph
The math. comb() method returns the number of ways picking k unordered outcomes from n possibilities, without repetition, also known as combinations.
combinations() do ? It returns r length subsequences of elements from the input iterable. Combinations are emitted in lexicographic sort order. So, if the input iterable is sorted, the combination tuples will be produced in sorted order.
The nPr (permutation) formula is: nPr = n!/(n-r)! The nCr (combination) formula is: nCr = n!/r!(
Here's one way to do it. There are lot's of micro-optimizations that could be made but their efficacy would depend on the sizes involved.
import collections as co
import itertools as it
def unique(list_):
return len(set(list_)) == len(list_)
def get_combos(branches):
by_parent = co.defaultdict(list)
for branch in branches:
by_parent[branch.p].append(branch)
combos = it.product(*by_parent.values())
return it.ifilter(lambda x: unique([b.c for b in x]), combos)
I'm pretty sure that this is at least hitting optimal complexity as I don't see a way to avoid looking at every combination that is unique by parent.
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