def test_self_factor_product(f1, f2):
f = get_product(f1, f1)
f = get_product(f, f1)
f = get_product(f, f1)
f = get_product(f, f1)
assert np.allclose(f.scope, np.array([1]))
assert np.allclose(f.card, np.array([2]))
assert np.allclose(f.val, np.array([0.11**5, 0.89**5]))
def test_f1_f2_product_commuative(f1, f2):
'''
f2
[2 1 phi]
------------
[0 0 0.59]
[1 0 0.41]
[0 1 0.22]
[1 1 0.78]
f1
[1 phi]
---------
[0 0.11]
[1 0.89]
f1 * f2
[1 2 phi]
-----------
[0 0 0.11 * 0.59]
[1 0 0.89 * 0.22]
[0 1 0.11 * 0.41]
[1 1 0.89 * 0.78]
'''
f = get_product(f2, f1)
assert np.allclose(f.scope, np.array([1, 2]))
assert np.allclose(f.card, np.array([2, 2]))
assert np.allclose(
f.val, np.array([0.11 * 0.59, 0.89 * 0.22, 0.11 * 0.41, 0.89 * 0.78]))
def test_f1_f2_product_log_space(f1, f2):
f = get_product(log(f1), log(f2), use_log_space=True)
f = exp(f)
assert np.allclose(f.scope, np.array([1, 2]))
assert np.allclose(f.card, np.array([2, 2]))
assert np.allclose(
f.val, np.array([0.11 * 0.59, 0.89 * 0.22, 0.11 * 0.41, 0.89 * 0.78]))
def test_another_marg():
f1 = Factor(scope=np.array([0]),
card=np.array([2], dtype=np.int32),
val=np.array([0.57, 0.43]))
f2 = Factor(scope=np.array([1, 0]),
card=np.array([2, 2], dtype=np.int32),
val=np.array([0.4, 0.6, 0.13, 0.87]))
f = get_product(f1, f2)
f_marg = get_marg(f, 1)
assert np.array_equal(f_marg.scope, np.array([0]))
assert np.array_equal(f_marg.card, np.array([2]))
assert np.allclose(f_marg.val, np.array([0.57, 0.43]))
def test_f2_f3_factor_product(f2, f3):
'''
f2
[2 1 phi]
------------
[0 0 0.59]
[1 0 0.41]
[0 1 0.22]
[1 1 0.78]
f3
[3 2 phi]
------------
[0 0 0.39]
[1 0 0.61]
[0 1 0.06]
[1 1 0.94]
f2 * f3
[1 2 3 phi]
--------------
[0 0 0 0.59 * 0.39]
[1 0 0 0.22 * 0.39]
[0 1 0 0.41 * 0.06]
[1 1 0 0.78 * 0.06]
[0 0 1 0.59 * 0.61]
[1 0 1 0.22 * 0.61]
[0 1 1 0.41 * 0.94]
[1 1 1 0.78 * 0.94]
'''
f = get_product(f2, f3)
assert np.allclose(f.scope, np.array([1, 2, 3]))
assert np.allclose(f.card, np.array([2, 2, 2]))
assert np.allclose(
f.val,
np.array([
0.59 * 0.39, 0.22 * 0.39, 0.41 * 0.06, 0.78 * 0.06, 0.59 * 0.61,
0.22 * 0.61, 0.41 * 0.94, 0.78 * 0.94
]))
def test_f1_f3_product(f1, f3):
'''
f1
[1 phi]
---------
[0 0.11]
[1 0.89]
f3
[3 2 phi]
------------
[0 0 0.39]
[1 0 0.61]
[0 1 0.06]
[1 1 0.94]
f1 * f3
[1 2 3 phi]
--------------
[0 0 0 0.11 * 0.39]
[1 0 0 0.89 * 0.39]
[0 1 0 0.11 * 0.06]
[1 1 0 0.89 * 0.06]
[1 0 1 0.11 * 0.61]
[1 0 1 0.89 * 0.61]
[1 1 1 0.11 * 0.94]
[1 1 1 0.89 * 0.94]
'''
f = get_product(f1, f3)
assert np.allclose(f.scope, np.array([1, 2, 3]))
assert np.allclose(f.card, np.array([2, 2, 2]))
assert np.allclose(
f.val,
np.array([
0.11 * 0.39, 0.89 * 0.39, 0.11 * 0.06, 0.89 * 0.06, 0.11 * 0.61,
0.89 * 0.61, 0.11 * 0.94, 0.89 * 0.94
]))
def test_f3_f4_product(f3, f4):
f = get_product(f3, f4)
assert np.allclose(f.scope, np.array([2, 3, 4, 5]))
assert np.allclose(f.card, np.array([2, 2, 2, 2]))
def get_clique_graph(elim_order,
induced_graph,
to_name):
clique_graph = nx.Graph()
factors = dict()
f_count = 1
# Store factors in dictionary
for name, dict_ in induced_graph.nodes_iter(data=True):
factor = dict_['factor']
factor_scope = set([to_name[i] for i in factor.scope])
factors[f_count] = {'factor': factor, 'scope': factor_scope}
f_count += 1
for node_name in elim_order:
# Any previous messages that should be included
# in this scope? (generated from other cliques)
edges = []
clique_scope = set()
for name, dict_ in clique_graph.nodes_iter(data=True):
# Do not connect to used messages
if dict_['msg_used']:
continue
if node_name in dict_['msg_scope']:
clique_scope.update(dict_['msg_scope'])
edges.append(name)
dict_['msg_used'] = True
# Find factors with node name
# in scope
fs = []
nodes_used = set()
factor_names = factors.keys()
for n in factor_names:
if node_name in factors[n]['scope']:
fs.append(factors[n]['factor'])
nodes_used.add(n)
del factors[n]
factor = None
if len(fs) > 0:
factor = get_product_from_list(fs, use_log_space=True)
factor_scope = set([to_name[i] for i in factor.scope])
clique_scope.update(factor_scope)
msg_scope = clique_scope.copy()
msg_scope.remove(node_name)
attr_dict = {'scope': clique_scope,
'msg_scope': msg_scope,
'nodes_used': nodes_used,
'msg_used': False,
'factor': factor,
'msg_upward': [],
'msg_downward': [],
'belief': None,
'children': [],
'parent': None}
clique_name = 'C' + str(len(clique_graph) + 1)
clique_graph.add_node(n=clique_name,
attr_dict=attr_dict)
for e in edges:
sepset = clique_graph.node[e]['scope'].intersection(clique_scope)
d = {'sepset': sepset}
clique_graph.add_edge(e, clique_name, d)
# See if any cliques are isolated
# if so, they had non-overlapping scopes
# So we can place them into any clique
isolated = []
C = None
for name, dict_ in clique_graph.nodes_iter(data=True):
if len(clique_graph.neighbors(name)) == 0:
isolated.append(name)
elif C is None:
C = name
for name in isolated:
factor = clique_graph.node[name]['factor']
f = clique_graph.node[C]['factor']
s = clique_graph.node[C]['scope']
f = get_product(f, factor, use_log_space=True)
clique_scope = set([to_name[i] for i in f.scope])
scope = clique_scope.update(s)
clique_graph.node[C]['factor'] = f
clique_graph.node[C]['scope'] = scope
clique_graph.remove_node(name)
# print("IS_CONNECTED: {0}".format(nx.is_connected(clique_graph)))
return clique_graph
