def test_io_fit():
d1 = DiscreteDistribution({True: 0.6, False: 0.4})
d2 = ConditionalProbabilityTable([
[True, 'A', 0.2],
[True, 'B', 0.8],
[False, 'A', 0.3],
[False, 'B', 0.7]], [d1])
d3 = ConditionalProbabilityTable([
['A', 0, 0.3],
['A', 1, 0.7],
['B', 0, 0.8],
['B', 1, 0.2]], [d2])
n1 = Node(d1)
n2 = Node(d2)
n3 = Node(d3)
model1 = BayesianNetwork()
model1.add_nodes(n1, n2, n3)
model1.add_edge(n1, n2)
model1.add_edge(n2, n3)
model1.bake()
model1.fit(X, weights=weights)
d1 = DiscreteDistribution({True: 0.2, False: 0.8})
d2 = ConditionalProbabilityTable([
[True, 'A', 0.7],
[True, 'B', 0.2],
[False, 'A', 0.4],
[False, 'B', 0.6]], [d1])
d3 = ConditionalProbabilityTable([
['A', 0, 0.9],
['A', 1, 0.1],
['B', 0, 0.0],
['B', 1, 1.0]], [d2])
n1 = Node(d1)
n2 = Node(d2)
n3 = Node(d3)
model2 = BayesianNetwork()
model2.add_nodes(n1, n2, n3)
model2.add_edge(n1, n2)
model2.add_edge(n2, n3)
model2.bake()
model2.fit(data_generator)
logp1 = model1.log_probability(X)
logp2 = model2.log_probability(X)
assert_array_almost_equal(logp1, logp2)
def BIC_score(data,Pa,card,struct):
#
z = []
for ii in range(len(Pa)):
zz = Pa[ii].copy()
zz.append(ii)
z.append(zz)
for ii in range(len(z)-1):
for jj in range(ii+1,len(z)):
if len(set(z[ii]) - (set(z[ii]) - set(z[jj])))>0:
z[ii] = list(set(z[ii]+z[jj]))
z[jj] = list(set(z[ii]+z[jj]))
ss=100000000
for ii in range(len(z)):
if len(z[ii])==len(z):
ss=0
#
model = BayesianNetwork()
model = BayesianNetwork.from_structure(data,struct)
BIC = model.log_probability(data).sum() - np.log(data.shape[0])*indep_params(Pa,card)/2 - ss
return BIC
