Python/graphs/gale_shapley_bigraph.py
Akash Shroff 25d9d819a2
Gale Shapley Algorithm (#2100)
* Gale Shapley Algorithm

Implementation of a Nobel prize-winning algorithm that determines a stable matching in a bipartite graph.

* Update graphs/gale_shapley_bigraph.py

Co-authored-by: Christian Clauss <cclauss@me.com>

* Fixed some flake8 issues.

* Updated it to donors and recipients

* description changes

Co-authored-by: Christian Clauss <cclauss@me.com>

* description changes

Co-authored-by: Christian Clauss <cclauss@me.com>

* description changes

Co-authored-by: Christian Clauss <cclauss@me.com>

* Edited the line lengths

* Update gale_shapley_bigraph.py

* Update gale_shapley_bigraph.py

Co-authored-by: Christian Clauss <cclauss@me.com>
2020-07-05 11:21:32 +02:00

45 lines
1.9 KiB
Python

from typing import List
def stable_matching(donor_pref: List[int], recipient_pref: List[int]) -> List[int]:
"""
Finds the stable match in any bipartite graph, i.e a pairing where no 2 objects
prefer each other over their partner. The function accepts the preferences of
oegan donors and recipients (where both are assigned numbers from 0 to n-1) and
returns a list where the index position corresponds to the donor and value at the
index is the organ recipient.
To better understand the algorithm, see also:
https://github.com/akashvshroff/Gale_Shapley_Stable_Matching (README).
https://www.youtube.com/watch?v=Qcv1IqHWAzg&t=13s (Numberphile YouTube).
>>> donor_pref = [[0, 1, 3, 2], [0, 2, 3, 1], [1, 0, 2, 3], [0, 3, 1, 2]]
>>> recipient_pref = [[3, 1, 2, 0], [3, 1, 0, 2], [0, 3, 1, 2], [1, 0, 3, 2]]
>>> print(stable_matching(donor_pref, recipient_pref))
[1, 2, 3, 0]
"""
assert len(donor_pref) == len(recipient_pref)
n = len(donor_pref)
unmatched_donors = list(range(n))
donor_record = [-1] * n # who the donor has donated to
rec_record = [-1] * n # who the recipient has received from
num_donations = [0] * n
while unmatched_donors:
donor = unmatched_donors[0]
donor_preference = donor_pref[donor]
recipient = donor_preference[num_donations[donor]]
num_donations[donor] += 1
rec_preference = recipient_pref[recipient]
prev_donor = rec_record[recipient]
if prev_donor != -1:
if rec_preference.index(prev_donor) > rec_preference.index(donor):
rec_record[recipient] = donor
donor_record[donor] = recipient
unmatched_donors.append(prev_donor)
unmatched_donors.remove(donor)
else:
rec_record[recipient] = donor
donor_record[donor] = recipient
unmatched_donors.remove(donor)
return donor_record