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| 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"] | |
| 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 | |