Warning

This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

# Source code for networkx.algorithms.moral

# -*- coding: utf-8 -*-
#   Julien Klaus <julien.klaus@uni-jena.de>
#
# Authors: Julien Klaus <julien.klaus@uni-jena.de>
r"""Function for computing the moral graph of a directed graph."""

import networkx as nx
from networkx.utils import not_implemented_for
import itertools

__all__ = ['moral_graph']

[docs]@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

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)
"""
if G is None:
raise ValueError("Expected NetworkX graph!")

H = G.to_undirected()
for preds in G.pred.values():
predecessors_combinations = itertools.combinations(preds, r=2)