def cond(n_1: Node, n_2: Node, e: Edge):
""
if e.type() == EdgeType.DIRECTED:
c1 = n_1 == e.start() and e.end() == n_2
return c1
else:
c1 = n_1 == e.start() and e.end() == n_2
c2 = n_2 == e.start() and e.end() == n_1
return c1 or c2
class EdgeTest(unittest.TestCase):
""
def setUp(self):
""
n1 = Node("m1", {})
n2 = Node("m2", {})
self.dedge = Edge(
edge_id="medge",
start_node=n1,
end_node=n2,
edge_type=EdgeType.DIRECTED,
data={"my": "data"},
)
self.uedge = Edge(
edge_id="uedge",
start_node=n1,
end_node=n2,
edge_type=EdgeType.UNDIRECTED,
data={"my": "data"},
)
def test_id(self):
""
self.assertEqual(self.uedge.id(), "uedge")
def test_type(self):
""
self.assertEqual(self.uedge.type(), EdgeType.UNDIRECTED)
def test_start(self):
self.assertEqual(self.uedge.start(), Node("m1", {}))
def test_end(self):
self.assertEqual(self.uedge.end(), Node("m2", {}))
def test_node_ids(self):
self.assertEqual(self.uedge.node_ids(), set(["m1", "m2"]))
def test_is_endvertice_true(self):
""
positive = self.uedge.is_endvertice(Node("m1", {}))
self.assertEqual(positive, True)
def test_is_endvertice_false(self):
""
negative = self.uedge.is_endvertice(Node("m3", {}))
self.assertEqual(negative, False)
def moralize(self) -> MarkovNetwork:
"""!
Moralize given chain graph: For any \f X,Y \in Pa_{K_i} \f add an edge
between them if it does not exist. Then drop the direction of edges.
"""
edges = self.edges()
enodes = set([frozenset([e.start(), e.end()]) for e in edges])
# add edges
for cid in range(len(self.ccomponents)):
pa_k_i: Set[NumCatRVariable] = self.parents_of_K(i=cid)
pa_k_i_cp = set([p for p in pa_k_i])
while len(pa_k_i_cp) > 0:
parent_node = pa_k_i_cp.pop()
for pnode in pa_k_i:
is_n_ind = self.is_node_independent_of(parent_node, pnode)
if (
is_n_ind is True
and frozenset([parent_node, pnode]).issubset(enodes) is False
):
e = Edge(
edge_id=str(uuid4()),
start_node=parent_node,
end_node=pnode,
edge_type=EdgeType.UNDIRECTED,
)
edges.add(e)
# drop orientation
nedges = set()
for e in edges:
if e.type() == EdgeType.DIRECTED:
ne = Edge(
edge_id=str(uuid4()),
start_node=e.start(),
end_node=e.end(),
edge_type=EdgeType.UNDIRECTED,
data=e.data(),
)
nedges.add(ne)
else:
nedges.add(e)
#
return MarkovNetwork(
gid=str(uuid4()), nodes=self.nodes(), edges=nedges, factors=self.factors()
)