58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
r"""Function for computing the moral graph of a directed graph."""
|
|
|
|
import itertools
|
|
|
|
from networkx.utils import not_implemented_for
|
|
|
|
__all__ = ["moral_graph"]
|
|
|
|
|
|
@not_implemented_for("undirected")
|
|
def moral_graph(G):
|
|
r"""Return the Moral Graph
|
|
|
|
Returns the moralized graph of a given directed graph.
|
|
|
|
Parameters
|
|
----------
|
|
G : NetworkX graph
|
|
Directed graph
|
|
|
|
Returns
|
|
-------
|
|
H : NetworkX graph
|
|
The undirected moralized graph of G
|
|
|
|
Raises
|
|
------
|
|
NetworkXNotImplemented
|
|
If `G` is undirected.
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.DiGraph([(1, 2), (2, 3), (2, 5), (3, 4), (4, 3)])
|
|
>>> G_moral = nx.moral_graph(G)
|
|
>>> G_moral.edges()
|
|
EdgeView([(1, 2), (2, 3), (2, 5), (2, 4), (3, 4)])
|
|
|
|
Notes
|
|
-----
|
|
A moral graph is an undirected graph H = (V, E) generated from a
|
|
directed Graph, where if a node has more than one parent node, edges
|
|
between these parent nodes are inserted and all directed edges become
|
|
undirected.
|
|
|
|
https://en.wikipedia.org/wiki/Moral_graph
|
|
|
|
References
|
|
----------
|
|
.. [1] Wray L. Buntine. 1995. Chain graphs for learning.
|
|
In Proceedings of the Eleventh conference on Uncertainty
|
|
in artificial intelligence (UAI'95)
|
|
"""
|
|
H = G.to_undirected()
|
|
for preds in G.pred.values():
|
|
predecessors_combinations = itertools.combinations(preds, r=2)
|
|
H.add_edges_from(predecessors_combinations)
|
|
return H
|