Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
65 lines
1.7 KiB
65 lines
1.7 KiB
__all__ = [
|
|
"assert_nodes_equal",
|
|
"assert_edges_equal",
|
|
"assert_graphs_equal",
|
|
"almost_equal",
|
|
]
|
|
|
|
|
|
def almost_equal(x, y, places=7):
|
|
return round(abs(x - y), places) == 0
|
|
|
|
|
|
def assert_nodes_equal(nodes1, nodes2):
|
|
# Assumes iterables of nodes, or (node,datadict) tuples
|
|
nlist1 = list(nodes1)
|
|
nlist2 = list(nodes2)
|
|
try:
|
|
d1 = dict(nlist1)
|
|
d2 = dict(nlist2)
|
|
except (ValueError, TypeError):
|
|
d1 = dict.fromkeys(nlist1)
|
|
d2 = dict.fromkeys(nlist2)
|
|
assert d1 == d2
|
|
|
|
|
|
def assert_edges_equal(edges1, edges2):
|
|
# Assumes iterables with u,v nodes as
|
|
# edge tuples (u,v), or
|
|
# edge tuples with data dicts (u,v,d), or
|
|
# edge tuples with keys and data dicts (u,v,k, d)
|
|
from collections import defaultdict
|
|
|
|
d1 = defaultdict(dict)
|
|
d2 = defaultdict(dict)
|
|
c1 = 0
|
|
for c1, e in enumerate(edges1):
|
|
u, v = e[0], e[1]
|
|
data = [e[2:]]
|
|
if v in d1[u]:
|
|
data = d1[u][v] + data
|
|
d1[u][v] = data
|
|
d1[v][u] = data
|
|
c2 = 0
|
|
for c2, e in enumerate(edges2):
|
|
u, v = e[0], e[1]
|
|
data = [e[2:]]
|
|
if v in d2[u]:
|
|
data = d2[u][v] + data
|
|
d2[u][v] = data
|
|
d2[v][u] = data
|
|
assert c1 == c2
|
|
# can check one direction because lengths are the same.
|
|
for n, nbrdict in d1.items():
|
|
for nbr, datalist in nbrdict.items():
|
|
assert n in d2
|
|
assert nbr in d2[n]
|
|
d2datalist = d2[n][nbr]
|
|
for data in datalist:
|
|
assert datalist.count(data) == d2datalist.count(data)
|
|
|
|
|
|
def assert_graphs_equal(graph1, graph2):
|
|
assert graph1.adj == graph2.adj
|
|
assert graph1.nodes == graph2.nodes
|
|
assert graph1.graph == graph2.graph
|
|
|