def setUp(self):
from gPy.Variables import Domain
self.bnm = BN(domain=Domain()) # don't use default domain
self.bnm.from_dnet(read_dnet('Asia.dnet'))
self.cptdict = self.bnm.factors
# 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])
]
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 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 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'))
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 bnfromfile(filename): x= BN(domain=self.domain); x.from_dnet(read_dnet(filename)); return x
self.assertRaises(CPTError,bnfromfile,open('Asia_wrong.dnet'))
from gPy.IO import read_dnet
from gPy.Models import BNM
from gPy.Demos import prod_gui
asia = BNM()
asia.from_dnet(read_dnet(open('Asia_lower.dnet')))
cancer = asia['Smoking'] * asia['Cancer']
cancer = cancer.sumout(['Smoking'])
visit = asia['VisitAsia'] * 1
prod_gui(visit,cancer)
from gPy.IO import read_dnet
from gPy.Models import BNM
from gPy.Demos import marginalise_gui
asia = BNM()
asia.from_dnet(read_dnet(open('Asia_lower.dnet')))
smoking = asia['Smoking'] * 1
cancer = asia['Cancer'] * 1
bronchitis = asia['Bronchitis'] * 1
smoking._data = [0.4,0.6] # gives better example
print cancer
print smoking
print bronchitis
foo = bronchitis.demo_mult(cancer)
joint = foo.demo_mult(smoking)
print joint
#show joint as a single factor with strings not numbers
#then marginalise joint, again showing numbers
def bnfromfile(filename):
x = BN(domain=self.domain)
x.from_dnet(read_dnet(filename))
return x
