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)
disp('two_independ', samples)
bn2 = BN(domain=Domain(), new_domain_variables={'a': [0,1,2], 'b':[0,1,2]})
bn2.add_cpts([CPT(Factor(variables=['a'], data=[1.0/3.0, 1.0/3.0, 1.0/3.0]),child='a')
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)
disp('two_independ', samples)
bn2 = BN(domain=Domain(),
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 = []
cbn_test_cases = cbn_small_test_cases + cbn_large_test_cases
def distribution_of(model):
"""Returns a normalised factor representing the joint instantiation
of the model.
"""
