From 4a52a71956a8d46fcb7294ac71734504bb09bcc2 Mon Sep 17 00:00:00 2001
From: S. Solomon Darnell
Date: Fri, 28 Mar 2025 21:52:21 -0500
Subject: two version of R2R are here

---
 .../site-packages/networkx/algorithms/moral.py     | 59 ++++++++++++++++++++++
 1 file changed, 59 insertions(+)
 create mode 100644 .venv/lib/python3.12/site-packages/networkx/algorithms/moral.py

(limited to '.venv/lib/python3.12/site-packages/networkx/algorithms/moral.py')

diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/moral.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/moral.py
new file mode 100644
index 00000000..e2acf80f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/networkx/algorithms/moral.py
@@ -0,0 +1,59 @@
+r"""Function for computing the moral graph of a directed graph."""
+
+import itertools
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["moral_graph"]
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable(returns_graph=True)
+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
-- 
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