def setUp(self):
# model a -> b
bn = {}
self.arr = []
bn[0] = BN(domain=Domain(),
new_domain_variables={
'a': [0, 1],
'b': [0, 1]
})
bn[0].add_cpts([
CPT(Factor(variables=['a'], data=[0.5, 0.5]), child='a'),
CPT(Factor(variables=['a', 'b'], data=[0.3, 0.7, 0.4, 0.6]),
child='b')
])
self.arr.append([('a', 'b')])
bn[1] = BN(domain=Domain(),
new_domain_variables={
'a': [0, 1],
'b': [0, 1],
'c': [0, 1]
})
bn[1].add_cpts([
CPT(Factor(variables=['a'], data=[0.5, 0.5]), child='a'),
CPT(Factor(variables=['a', 'b'], data=[0.3, 0.7, 0.4, 0.6]),
child='b'),
CPT(Factor(variables=['c', 'b'], data=[0.1, 0.9, 0.2, 0.8]),
child='c')
])
self.arr.append([('a', 'b'), ('b', 'c')])
self.cbn = [CBN.from_bn(bn[i]) for i in bn.keys()]
def testdnet(self):
from gPy.IO import read_dnet
from gPy.Models import BN
from gPy.Variables import Domain
bnm = BN(domain=Domain())
bnm.from_dnet(read_dnet('Asia.dnet'))
self.samegraph(bnm.adg(), self.asia_adg)
for name, cpt_in_file in self.asia_cpts.items():
cpt = bnm[name]
self.samecpt(cpt, cpt_in_file, cpt.child())
def rand_bn(vs, max_potential_parents=15):
model = BN(domain=Domain(), new_domain_variables=vs)
for child in vs.keys():
parents = list(model.variables())
too_many = len(parents) - max_potential_parents
if too_many > 0:
for i in xrange(too_many):
parents.remove(choice(parents))
fv = rand_subset(parents) | set([child])
n = reduce(operator.mul, [len(vs[v]) for v in fv])
f = Factor(variables=fv,
data=rand_factor_data(n),
domain=model,
check=True)
cpt = CPT(f, child, True, True)
model *= cpt
return model
def generate_dense_bn(density, num_vars=8, num_vals=3):
if density > num_vars:
raise RuntimeError, 'density must be less than number of variables'
vars, parents = generate_dense_parents(density, num_vars)
vals = dict([(var, frozenset([i for i in xrange(num_vals)]))
for var in vars])
bn = BN(domain=Domain(), new_domain_variables=vals)
for child in vars:
if child in parents:
n = num_vals**(len(parents[child]) + 1)
else:
n = num_vals
parents[child] = frozenset()
f = Factor(variables=frozenset([child]) | parents[child],
data=rand_factor_data(n),
domain=bn,
check=True)
bn *= CPT(f, child, True, True)
return bn
def setUp(self):
from gPy.Variables import Domain
bnm = BN(domain=Domain())
bnm.from_dnet(read_dnet('Asia.dnet'))
self.hypergraph = bnm._hypergraph
self.adg = bnm._adg
self.tarjan = UGraph(range(1,11),
((1,2),(1,3),(2,3),(2,10),(3,10),(4,5),
(4,7),(5,6),(5,9),(5,7),(6,7),(6,9),
(7,8),(7,9),(8,9),(8,10),(9,10)))
self.tarjan2 = UGraph(range(1,10),
((1,4),(1,3),(2,3),(2,7),(3,5),(3,6),
(4,5),(4,8),(5,6),(5,8),(6,7),(6,9),
(7,9),(8,9)))
self.tarjan3 = UGraph(range(1,10),
((1,4),(1,3),(2,3),(2,7),(3,5),(3,6),
(4,5),(4,8),(5,6),(5,8),(6,7),(6,9),
(7,9),(8,9),
(3,4),(3,7),(4,6),(4,7),(5,7),(6,8),(7,8)))
self.tarjanh1 = Hypergraph([[3,4],[2,4],[1,2,3]])
self.tarjanh2 = Hypergraph([[3,4],[2,4],[1,2,3],[2,3,4]])
self.graph1 = UGraph('ABCDEF',('AB','AC','BD','CE','EF'))
self.graph2 = UGraph('ABCDEF',('AB','AC','BD','CE','EF','BC','CD','DE'))
def setUp(self):
from gPy.Variables import Domain
self.domain = Domain()
self.bnm = BN(domain=self.domain)
self.bnm.from_dnet(read_dnet('Asia.dnet'))
self.cptdict = {}
# taken directly from Netica output
self.marginals = [
Factor((('VisitAsia'), ), [0.99, 0.01]),
Factor((('Tuberculosis'), ), [0.9896, 0.0104]),
Factor((('Smoking'), ), [0.5, 0.5]),
Factor((('Cancer'), ), [0.945, 0.055]),
Factor((('TbOrCa'), ), [0.93517, 0.064828]),
Factor((('XRay'), ), [0.11029, 0.88971]),
Factor((('Bronchitis'), ), [0.55, 0.45]),
Factor((('Dyspnea'), ), [0.56403, 0.43597])
]
# taken directly from Netica output
self.cond_marginals = [
Factor((('VisitAsia'), ), [0.95192, 0.048077]),
Factor((('Tuberculosis'), ), [0, 1]),
Factor((('Smoking'), ), [0.52381, 0.47619]),
#other marginals are conditional on these values
#Factor((('Cancer'),),
# [1,0]),
#Factor((('TbOrCa'),),
# [0,1]),
Factor((('XRay'), ), [0.98, 0.02]),
Factor((('Bronchitis'), ), [0.55714, 0.44286]),
Factor((('Dyspnea'), ), [0.21143, 0.78857])
]
for cpt in self.bnm:
self.cptdict[cpt.child()] = cpt
self.rawdata = read_csv(open('alarm_1K.dat'))
from gPy.Examples import minibn, asia
from gPy.Models import FR, BN
from gPy.Parameters import Factor, CPT
from gPy.Variables import Domain
from random import choice, randrange, uniform, shuffle
import operator, unittest, pickle
xor = BN(domain=Domain(),
new_domain_variables={
'a': [0, 1],
'b': [0, 1],
'c': [0, 1]
})
xor.add_cpts([
CPT(Factor(variables=['a'], data=[0.5, 0.5]), child='a'),
CPT(Factor(variables=['b'], data=[0.5, 0.5]), child='b'),
CPT(Factor(variables=['c', 'a', 'b'],
data=[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0]),
child='c')
])
cbn_small_names = ['xor', 'minibn', 'asia']
cbn_small_test_cases = [xor, minibn, asia]
cbn_large_names = ['alarm', 'insurance', 'carpo']
try:
# load the pickled large Bayes nets.
cbn_large_test_cases = map(
lambda fn: pickle.load(open('networks/' + fn + '_bn.pck', 'r')),
cbn_large_names)
except:
cbn_large_names = []
cbn_large_test_cases = []
def disp(fn, samples):
f = open(fn, 'w')
fact = samples.makeFactor(samples.variables())
for var in fact.variables():
print >> f, var,
print >> f, 'count'
for inst in fact.insts():
for i in inst:
print >> f, i,
print >> f, fact[inst]
f.close()
bn0 = BN(domain=Domain(), new_domain_variables={'a': [0, 1], 'b': [0, 1]})
bn0.add_cpts([
CPT(Factor(variables=['a'], data=[0.5, 0.5]), child='a'),
CPT(Factor(variables=['a', 'b'], data=[0.3, 0.7, 0.4, 0.6]), child='b')
])
w = CausalWorld(bn0)
samples = w.observe(10000)
disp('two_depend', samples)
bn1 = BN(domain=Domain(), new_domain_variables={'a': [0, 1], 'b': [0, 1]})
bn1.add_cpts([
CPT(Factor(variables=['a'], data=[0.5, 0.5]), child='a'),
CPT(Factor(variables=['b'], data=[0.3, 0.7]), child='b')
])
w = CausalWorld(bn1)
samples = w.observe(10000)
def bnfromfile(filename):
x = BN(domain=self.domain)
x.from_dnet(read_dnet(filename))
return x
