ground: 1.0,
sun: 4.0
}, 2.5))
height.set_density_parameters(
BetaParameters(0.1, {growth: 1}, 0.5, {growth: 0.5}))
diameter.set_density_parameters(ExponentialParameters(0.01, {growth: 0.2}))
children.set_density_parameters(
ExponentialParameters(0.1, {
ground: 1.0,
height: 1.0
}))
mcmc_ask = MCMC(bn, 1000)
evidence = {age: EvEqual(2)}
print "PosteriorMarginal:"
pm = mcmc_ask.calculate_PosteriorMarginal([age, height], evidence, NDGauss)
#pm=mcmc_ask.calculate_PosteriorMarginal([height],evidence,Gauss)
print pm
print "PriorMarginal:"
pm = mcmc_ask.calculate_PriorMarginal([age], NDGauss)
print pm
#pm=mcmc_ask.calculate_PriorMarginal([height,diameter],Gauss)
pm = mcmc_ask.calculate_PriorMarginal([height], NDGauss)
print pm
#->lambda=2.0
age_parameters=ExponentialParameters(0.0,{})
age.set_density_parameters(age_parameters)
#Semantics: plants start at 0.1 meters underground and grow each year by 1 meter.
# variance is 0.3
height_parameters=GaussParameters(-0.1,{age:1},0.3)
height.set_density_parameters(height_parameters)
#Semantics: At small age: low alpha, high beta -> skew to the left: thin plants
# At higher age: high alpha, low beta -> skew to the right: thick plants
diameter_parameters=BetaParameters(-10.0,{age:4.0},10.0,{age:-4.0})
diameter.set_density_parameters(diameter_parameters)
mcmc_ask=MCMC(bn,1000,convergence_test=ConvergenceTestSimpleCounting(500))
print "------PriorMarginal:------"
pm=mcmc_ask.calculate_PriorMarginal([age],NDGauss)
print pm
print "Ground truth: mu=0.5 C=[0.25]"
pm=mcmc_ask.calculate_PriorMarginal([height],NDGauss)
print pm
print ""
print "------PosteriorMarginal:------"
pm=mcmc_ask.calculate_PosteriorMarginal([age,height],{age:EvEqual(2)},NDGauss)
alarm = DiscreteNode("Alarm", ["Ringing", "Silent", "Destroyed"])
bn.add_node(burglary)
bn.add_node(alarm)
bn.add_edge(burglary, alarm)
#Parametrize the network
burglary_cpt = numpy.array([0.2, 0.8])
burglary.set_probability_table(burglary_cpt, [burglary])
alarm_cpt = numpy.array([[0.8, 0.15, 0.05], [0.05, 0.9, 0.05]])
alarm.set_probability_table(alarm_cpt, [burglary, alarm])
#Get some inference object
mcmc_ask = MCMC(bn, 5000, transition_model=GibbsTransitionModel())
#Do some Inferences
evidence = {burglary: EvEq("Intruder")}
print "-------ProbabilityOfEvidence:-------"
poe = mcmc_ask.calculate_PoE(evidence)
print "p(evidence=Intruder)=" + str(poe)
print "Ground truth=0.2\n"
print "-------PosteriorMarginal:-------"
pm = mcmc_ask.calculate_PosteriorMarginal([alarm], evidence, ProbabilityTable)
print "P(alarm|burglary=Intruder)=" + str(pm)
print "Ground truth=[0.8, 0.15, 0.05]\n"
print "-------PriorMarginal:-------"
