def createBloodtypeDist(genotypeVar, bloodtypeVar):
V = Variable(genotypeVar, genotypes)
W = Variable(bloodtypeVar, bloodtypes)
return MultivariateFunction([V, W], {
('AA', 'A'): 1.0,
('AA', 'B'): 0.0,
('AA', 'AB'): 0.0,
('AA', 'O'): 0.0,
('AB', 'A'): 0.0,
('AB', 'B'): 0.0,
('AB', 'AB'): 1.0,
('AB', 'O'): 0.0,
('AO', 'A'): 1.0,
('AO', 'B'): 0.0,
('AO', 'AB'): 0.0,
('AO', 'O'): 0.0,
('BB', 'A'): 0.0,
('BB', 'AB'): 0.0,
('BB', 'B'): 1.0,
('BB', 'O'): 0.0,
('BO', 'A'): 0.0,
('BO', 'AB'): 0.0,
('BO', 'B'): 1.0,
('BO', 'O'): 0.0,
('OO', 'A'): 0.0,
('OO', 'AB'): 0.0,
('OO', 'B'): 0.0,
('OO', 'O'): 1.0})
def createSynthesizedGenotypeDist(motherGeneVar, fatherGeneVar, genotypeVar):
V = Variable(motherGeneVar, genes)
W = Variable(fatherGeneVar, genes)
X = Variable(genotypeVar, genotypes)
return MultivariateFunction([V, W, X], {
('A', 'A', 'AA'): 1.0,
('A', 'A', 'AB'): 0.0,
('A', 'A', 'AO'): 0.0,
('A', 'A', 'BB'): 0.0,
('A', 'A', 'BO'): 0.0,
('A', 'A', 'OO'): 0.0,
('A', 'B', 'AA'): 0.0,
('A', 'B', 'AB'): 1.0,
('A', 'B', 'AO'): 0.0,
('A', 'B', 'BB'): 0.0,
('A', 'B', 'BO'): 0.0,
('A', 'B', 'OO'): 0.0,
('A', 'O', 'AA'): 0.0,
('A', 'O', 'AB'): 0.0,
('A', 'O', 'AO'): 1.0,
('A', 'O', 'BB'): 0.0,
('A', 'O', 'BO'): 0.0,
('A', 'O', 'OO'): 0.0,
('B', 'A', 'AA'): 0.0,
('B', 'A', 'AB'): 1.0,
('B', 'A', 'AO'): 0.0,
('B', 'A', 'BB'): 0.0,
('B', 'A', 'BO'): 0.0,
('B', 'A', 'OO'): 0.0,
('B', 'B', 'AA'): 0.0,
('B', 'B', 'AB'): 0.0,
('B', 'B', 'AO'): 0.0,
('B', 'B', 'BB'): 1.0,
('B', 'B', 'BO'): 0.0,
('B', 'B', 'OO'): 0.0,
('B', 'O', 'AA'): 0.0,
('B', 'O', 'AB'): 0.0,
('B', 'O', 'AO'): 0.0,
('B', 'O', 'BB'): 0.0,
('B', 'O', 'BO'): 1.0,
('B', 'O', 'OO'): 0.0,
('O', 'A', 'AA'): 0.0,
('O', 'A', 'AB'): 0.0,
('O', 'A', 'AO'): 1.0,
('O', 'A', 'BB'): 0.0,
('O', 'A', 'BO'): 0.0,
('O', 'A', 'OO'): 0.0,
('O', 'B', 'AA'): 0.0,
('O', 'B', 'AB'): 0.0,
('O', 'B', 'AO'): 0.0,
('O', 'B', 'BB'): 0.0,
('O', 'B', 'BO'): 1.0,
('O', 'B', 'OO'): 0.0,
('O', 'O', 'AA'): 0.0,
('O', 'O', 'AB'): 0.0,
('O', 'O', 'AO'): 0.0,
('O', 'O', 'BB'): 0.0,
('O', 'O', 'BO'): 0.0,
('O', 'O', 'OO'): 1.0})
def createGenotypePrior(name):
V = Variable(name, genotypes)
return MultivariateFunction([V], {
('AA',): 1./6,
('AB',): 1./6,
('AO',): 1./6,
('BB',): 1./6,
('BO',): 1./6,
('OO',): 1./6})
def createTransmissionFunction(messageBit, transmittedBit, noiseProb):
M1 = Variable(messageBit, ('0', '1'))
A1 = Variable(transmittedBit, ('0', '1'))
result = MultivariateFunction([M1, A1], {
('0', '0'): 1.0 - noiseProb,
('0', '1'): noiseProb,
('1', '0'): noiseProb,
('1', '1'): 1.0 - noiseProb})
return result
def createIdentityFunction(messageBit, encodedBit):
M1 = Variable(messageBit, ('0', '1'))
A1 = Variable(encodedBit, ('0', '1'))
result = MultivariateFunction([M1, A1], {
('0', '0'): 1.0,
('0', '1'): 0.0,
('1', '0'): 0.0,
('1', '1'): 1.0})
return result
def createEncodingFunction(historyBit1, historyBit2, messageBit):
H1 = Variable(historyBit1, ('0', '1'))
H2 = Variable(historyBit2, ('0', '1'))
M1 = Variable(messageBit, ('0', '1'))
result = MultivariateFunction([H1, H2, M1], {
('0', '0', '0'): 1.0,
('0', '0', '1'): 0.0,
('0', '1', '0'): 0.0,
('0', '1', '1'): 1.0,
('1', '0', '0'): 0.0,
('1', '0', '1'): 1.0,
('1', '1', '0'): 1.0,
('1', '1', '1'): 0.0})
return result
def createInheritedGeneDist(genotypeVar, geneVar):
V = Variable(genotypeVar, genotypes)
W = Variable(geneVar, genes)
return MultivariateFunction([V, W], {
('AA', 'A'): 1.0,
('AA', 'B'): 0.0,
('AA', 'O'): 0.0,
('AB', 'A'): 0.5,
('AB', 'B'): 0.5,
('AB', 'O'): 0.0,
('AO', 'A'): 0.5,
('AO', 'B'): 0.0,
('AO', 'O'): 0.5,
('BB', 'A'): 0.0,
('BB', 'B'): 1.0,
('BB', 'O'): 0.0,
('BO', 'A'): 0.0,
('BO', 'B'): 0.5,
('BO', 'O'): 0.5,
('OO', 'A'): 0.0,
('OO', 'B'): 0.0,
('OO', 'O'): 1.0})
"""
Example Bayesian network. P(P="yes" | S="-ve", B="-ve", U="-ve") = 0.1021.
"""
from variable import Variable
from bayes import MultivariateFunction, BayesianNetwork, conditionalProb
P = Variable('P', ['yes', 'no'])
L = Variable('L', ['u', 'd'])
S = Variable('S', ['-ve', '+ve'])
B = Variable('B', ['-ve', '+ve'])
U = Variable('U', ['-ve', '+ve'])
fP = MultivariateFunction([P], {('yes', ): 0.87, ('no', ): 0.13})
fL = MultivariateFunction([P, L], {
('yes', 'u'): 0.1,
('yes', 'd'): 0.9,
('no', 'u'): 0.99,
('no', 'd'): 0.01
})
fS = MultivariateFunction(
[P, S], {
('yes', '-ve'): 0.1,
('yes', '+ve'): 0.9,
('no', '-ve'): 0.99,
('no', '+ve'): 0.01
})
fB = MultivariateFunction([L, B], {
('u', '-ve'): 0.9,
('u', '+ve'): 0.1,
('d', '-ve'): 0.3,
def createMessageFunction(name):
M1 = Variable(name, ('0', '1'))
return MultivariateFunction([M1], {('0',): 0.5, ('1',): 0.5})