def test_junction_tree_directed_unconnected_edges(): B = nx.DiGraph() B.add_edges_from([("A", "B"), ("C", "D"), ("E", "F")]) G = junction_tree(B) J = nx.Graph() J.add_nodes_from([("A", "B"), ("C", "D"), ("E", "F")]) assert nx.is_isomorphic(G, J)
def test_junction_tree_directed_cascade(): B = nx.DiGraph() B.add_edges_from([("A", "B"), ("B", "C"), ("C", "D")]) G = junction_tree(B) J = nx.Graph() J.add_edges_from([ (("A", "B"), ("B", )), (("B", ), ("B", "C")), (("B", "C"), ("C", )), (("C", ), ("C", "D")), ]) assert nx.is_isomorphic(G, J)
from networkx.algorithms import moral from networkx.algorithms.tree.decomposition import junction_tree from networkx.drawing.nx_agraph import graphviz_layout as layout import matplotlib.pyplot as plt B = nx.DiGraph() B.add_nodes_from(["A", "B", "C", "D", "E", "F"]) B.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("B", "F"), ("C", "E"), ("E", "F")]) options = {"with_labels": True, "node_color": "white", "edgecolors": "blue"} bayes_pos = layout(B, prog="neato") ax1 = plt.subplot(1, 3, 1) plt.title("Bayesian Network") nx.draw_networkx(B, pos=bayes_pos, **options) mg = moral.moral_graph(B) plt.subplot(1, 3, 2, sharex=ax1, sharey=ax1) plt.title("Moralized Graph") nx.draw_networkx(mg, pos=bayes_pos, **options) jt = junction_tree(B) plt.subplot(1, 3, 3) plt.title("Junction Tree") nsize = [2000 * len(n) for n in list(jt.nodes())] nx.draw_networkx(jt, pos=layout(jt, prog="neato"), node_size=nsize, **options) plt.tight_layout() plt.show()