Gale Shapley Algorithm (TheAlgorithms#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 <[email protected]>

* Fixed some flake8 issues.

* Updated it to donors and recipients

* description changes

Co-authored-by: Christian Clauss <[email protected]>

* description changes

Co-authored-by: Christian Clauss <[email protected]>

* description changes

Co-authored-by: Christian Clauss <[email protected]>

* Edited the line lengths

* Update gale_shapley_bigraph.py

* Update gale_shapley_bigraph.py

Co-authored-by: Christian Clauss <[email protected]>

graphs/gale_shapley_bigraph.py

@@ -0,0 +1,44 @@
+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