diff env/lib/python3.9/site-packages/networkx/algorithms/moral.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/env/lib/python3.9/site-packages/networkx/algorithms/moral.py	Mon Mar 22 18:12:50 2021 +0000
@@ -0,0 +1,47 @@
+r"""Function for computing the moral graph of a directed graph."""
+
+from networkx.utils import not_implemented_for
+import itertools
+
+__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
+
+        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)
+        H.add_edges_from(predecessors_combinations)
+    return H