def setUp(self):
# create a discrete network
G = bayesnet.BNet('Water Sprinkler Bayesian Network')
c, s, r, w = [
G.add_v(bayesnet.BVertex(nm, True, 2)) for nm in 'c s r w'.split()
]
for ep in [(c, r), (c, s), (r, w), (s, w)]:
G.add_e(graph.DirEdge(len(G.e), *ep))
G.InitDistributions()
c.setDistributionParameters([0.5, 0.5])
s.setDistributionParameters([0.5, 0.9, 0.5, 0.1])
r.setDistributionParameters([0.8, 0.2, 0.2, 0.8])
w.distribution[:, 0, 0] = [0.99, 0.01]
w.distribution[:, 0, 1] = [0.1, 0.9]
w.distribution[:, 1, 0] = [0.1, 0.9]
w.distribution[:, 1, 1] = [0.0, 1.0]
self.c = c
self.s = s
self.r = r
self.w = w
self.BNet = G
# create a simple continuous network
G2 = bayesnet.BNet('Gaussian Bayesian Network')
a, b = [
G2.add_v(bayesnet.BVertex(nm, False, 1)) for nm in 'a b'.split()
]
for ep in [(a, b)]:
G2.add_e(graph.DirEdge(len(G2.e), *ep))
G2.InitDistributions()
a.setDistributionParameters(mu=1.0, sigma=1.0)
b.setDistributionParameters(mu=1.0, sigma=1.0, wi=2.0)
self.a = a
self.b = b
self.G2 = G2
def setUp(self):
# create a discrete network
G = bayesnet.BNet('Water Sprinkler Bayesian Network')
c, s, r, w = [G.add_v(bayesnet.BVertex(nm, True, 2)) for \
nm in 'c s r w'.split()]
for ep in [(c, r), (c, s), (r, w), (s, w)]:
G.add_e(graph.DirEdge(len(G.e), *ep))
G.InitDistributions()
c.setDistributionParameters([0.5, 0.5])
s.setDistributionParameters([0.5, 0.9, 0.5, 0.1])
r.setDistributionParameters([0.8, 0.2, 0.2, 0.8])
w.distribution[:, 0, 0] = [0.99, 0.01]
w.distribution[:, 0, 1] = [0.1, 0.9]
w.distribution[:, 1, 0] = [0.1, 0.9]
w.distribution[:, 1, 1] = [0.0, 1.0]
self.c = c
self.s = s
self.r = r
self.w = w
self.BNet = G
def setUp(self):
# create the network
G = bayesnet.BNet('Asia Bayesian Network')
visit, smoking, tuberculosis, bronchitis, lung, ou, Xray, dyspnoea = \
[G.add_v(bayesnet.BVertex( nm, True, 2)) for nm in \
'visit smoking tuberculosis bronchitis lung ou Xray dyspnoea'.split()]
for ep in [(visit,tuberculosis), (tuberculosis, ou), (smoking,lung), \
(lung, ou), (ou, Xray), (smoking, bronchitis), \
(bronchitis, dyspnoea), (ou, dyspnoea)]:
G.add_e(graph.DirEdge(len(G.e), *ep))
G.InitDistributions()
visit.setDistributionParameters([0.99, 0.01])
tuberculosis.distribution[:, 0] = [0.99, 0.01]
tuberculosis.distribution[:, 1] = [0.95, 0.05]
smoking.setDistributionParameters([0.5, 0.5])
lung.distribution[:, 0] = [0.99, 0.01]
lung.distribution[:, 1] = [0.9, 0.1]
ou.distribution[:, 0, 0] = [1, 0]
ou.distribution[:, 0, 1] = [0, 1]
ou.distribution[:, 1, 0] = [0, 1]
ou.distribution[:, 1, 1] = [0, 1]
Xray.distribution[:, 0] = [0.95, 0.05]
Xray.distribution[:, 1] = [0.02, 0.98]
bronchitis.distribution[:, 0] = [0.7, 0.3]
bronchitis.distribution[:, 1] = [0.4, 0.6]
dyspnoea.distribution[{'bronchitis': 0, 'ou': 0}] = [0.9, 0.1]
dyspnoea.distribution[{'bronchitis': 1, 'ou': 0}] = [0.2, 0.8]
dyspnoea.distribution[{'bronchitis': 0, 'ou': 1}] = [0.3, 0.7]
dyspnoea.distribution[{'bronchitis': 1, 'ou': 1}] = [0.1, 0.9]
self.visit = visit
self.tuberculosis = tuberculosis
self.smoking = smoking
self.lung = lung
self.ou = ou
self.Xray = Xray
self.bronchitis = bronchitis
self.dyspnoea = dyspnoea
self.BNet = G
def testSEM(self):
return
N = 700
# sample the network N times, delete some data
cases = self.BNet.Sample(
N) # cases = [{'c':0,'s':1,'r':0,'w':1},{...},...]
for i in range(25):
case = cases[3 * i]
rand = random.sample(['c', 's', 'r', 'w'], 1)[0]
case[rand] = '?'
for i in range(3):
case = cases[3 * i]
rand = random.sample(['c', 's', 'r', 'w'], 1)[0]
case[rand] = '?'
G = bayesnet.BNet('Water Sprinkler Bayesian Network2')
c, s, r, w = [G.add_v(bayesnet.BVertex(nm, True, 2)) for nm \
in 'c s r w'.split()]
G.InitDistributions()
# Test SEMLearning
struct_engine = SEMLearningEngine(G)
struct_engine.SEMLearningApprox(cases)
struct_engine.SaveInFile('./output/testSEM05.txt', G, \
struct_engine.BNet, struct_engine)
struct_engine.EMLearning(cases, 10)
struct_engine.SaveInFile('./output/testSEM205.txt', G, \
struct_engine.BNet, struct_engine)
print '1 OK!'
G1 = bayesnet.BNet('Water Sprinkler Bayesian Network2')