Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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66 lines
1.7 KiB
66 lines
1.7 KiB
4 years ago
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__all__ = [
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"assert_nodes_equal",
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"assert_edges_equal",
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"assert_graphs_equal",
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"almost_equal",
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]
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def almost_equal(x, y, places=7):
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return round(abs(x - y), places) == 0
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def assert_nodes_equal(nodes1, nodes2):
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# Assumes iterables of nodes, or (node,datadict) tuples
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nlist1 = list(nodes1)
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nlist2 = list(nodes2)
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try:
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d1 = dict(nlist1)
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d2 = dict(nlist2)
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except (ValueError, TypeError):
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d1 = dict.fromkeys(nlist1)
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d2 = dict.fromkeys(nlist2)
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assert d1 == d2
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def assert_edges_equal(edges1, edges2):
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# Assumes iterables with u,v nodes as
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# edge tuples (u,v), or
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# edge tuples with data dicts (u,v,d), or
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# edge tuples with keys and data dicts (u,v,k, d)
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from collections import defaultdict
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d1 = defaultdict(dict)
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d2 = defaultdict(dict)
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c1 = 0
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for c1, e in enumerate(edges1):
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u, v = e[0], e[1]
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data = [e[2:]]
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if v in d1[u]:
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data = d1[u][v] + data
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d1[u][v] = data
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d1[v][u] = data
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c2 = 0
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for c2, e in enumerate(edges2):
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u, v = e[0], e[1]
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data = [e[2:]]
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if v in d2[u]:
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data = d2[u][v] + data
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d2[u][v] = data
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d2[v][u] = data
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assert c1 == c2
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# can check one direction because lengths are the same.
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for n, nbrdict in d1.items():
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for nbr, datalist in nbrdict.items():
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assert n in d2
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assert nbr in d2[n]
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d2datalist = d2[n][nbr]
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for data in datalist:
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assert datalist.count(data) == d2datalist.count(data)
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def assert_graphs_equal(graph1, graph2):
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assert graph1.adj == graph2.adj
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assert graph1.nodes == graph2.nodes
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assert graph1.graph == graph2.graph
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