Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/moral.py @ 0:4f3585e2f14b draft default tip
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author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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1 r"""Function for computing the moral graph of a directed graph.""" |
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2 |
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3 from networkx.utils import not_implemented_for |
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4 import itertools |
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5 |
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6 __all__ = ["moral_graph"] |
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7 |
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8 |
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9 @not_implemented_for("undirected") |
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10 def moral_graph(G): |
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11 r"""Return the Moral Graph |
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12 |
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13 Returns the moralized graph of a given directed graph. |
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14 |
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15 Parameters |
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16 ---------- |
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17 G : NetworkX graph |
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18 Directed graph |
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19 |
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20 Returns |
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21 ------- |
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22 H : NetworkX graph |
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23 The undirected moralized graph of G |
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24 |
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25 Notes |
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26 ------ |
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27 A moral graph is an undirected graph H = (V, E) generated from a |
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28 directed Graph, where if a node has more than one parent node, edges |
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29 between these parent nodes are inserted and all directed edges become |
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30 undirected. |
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31 |
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32 https://en.wikipedia.org/wiki/Moral_graph |
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33 |
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34 References |
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35 ---------- |
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36 .. [1] Wray L. Buntine. 1995. Chain graphs for learning. |
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37 In Proceedings of the Eleventh conference on Uncertainty |
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38 in artificial intelligence (UAI'95) |
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39 """ |
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40 if G is None: |
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41 raise ValueError("Expected NetworkX graph!") |
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42 |
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43 H = G.to_undirected() |
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44 for preds in G.pred.values(): |
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45 predecessors_combinations = itertools.combinations(preds, r=2) |
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46 H.add_edges_from(predecessors_combinations) |
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47 return H |