import numpy
from primo.networks import BayesianNetwork
from primo.nodes import ContinuousNodeFactory
cnf = ContinuousNodeFactory()
age = cnf.createGaussNode("Plant_age")
#parameterization
age_b0=numpy.array([4])
age_variance=numpy.array([[2]])
age.set_density_parameters(age_b0,age_variance)
for i in range(8):
print age.sample()
from primo.densities import GaussParameters
from primo.densities import NDGauss
from primo.inference.mcmc import MCMC
from primo.evidence import EvidenceEqual as EvEqual
from primo.evidence import EvidenceInterval as EvInterval
import numpy
# Construct some simple BayesianNetwork. In this example it models
# the linear relationship between the age of a plant and the height that
# it has grown up to (+noise)
bn = BayesianNetwork()
cnf = ContinuousNodeFactory()
# create the nodes
age = cnf.createExponentialNode("Age")
sun = cnf.createBetaNode("Sun")
ground = cnf.createGaussNode("Ground")
growth = cnf.createGaussNode("Growth")
height = cnf.createBetaNode("Height")
diameter = cnf.createExponentialNode("Diameter")
children = cnf.createExponentialNode("Children")
# add the nodes to the network
bn.add_node(age)
bn.add_node(sun)
bn.add_node(ground)
bn.add_node(growth)
#About this example:
#This example shows how approximate inference can be used query a purely continuous
#bayesian network. At first that network is being constructed and afterwards it
#is passed to an MCMC object that is used to answer several kinds of questions:
#-Prior marginal
#-Posterior marginal
#-Probability of evidence
#-Maximum a-posteriori hypothesis
#Construct some simple BayesianNetwork.
#topology
bn = BayesianNetwork()
cnf=ContinuousNodeFactory()
age = cnf.createExponentialNode("Plant_age")
height = cnf.createGaussNode("Plant_height")
diameter = cnf.createBetaNode("Plant_diameter")
bn.add_node(age)
bn.add_node(height)
bn.add_node(diameter)
bn.add_edge(age,height)
bn.add_edge(age,diameter)
#parameterization
#Semantics: Many young plants and the higher the age the lower the probabilty
#->lambda=2.0
age_parameters=ExponentialParameters(0.0,{})
from primo.densities import BetaParameters
from primo.densities import GaussParameters
from primo.densities import NDGauss
from primo.inference.mcmc import MCMC
from primo.evidence import EvidenceEqual as EvEqual
from primo.evidence import EvidenceInterval as EvInterval
import numpy
#Construct some simple BayesianNetwork. In this example it models
#the linear relationship between the age of a plant and the height that
#it has grown up to (+noise)
bn = BayesianNetwork()
cnf = ContinuousNodeFactory()
#create the nodes
age = cnf.createExponentialNode("Age")
sun = cnf.createBetaNode("Sun")
ground = cnf.createGaussNode("Ground")
growth = cnf.createGaussNode("Growth")
height = cnf.createBetaNode("Height")
diameter = cnf.createExponentialNode("Diameter")
children = cnf.createExponentialNode("Children")
#add the nodes to the network
bn.add_node(age)
bn.add_node(sun)
bn.add_node(ground)
bn.add_node(growth)