class ImportExportTest(unittest.TestCase):
def setUp(self):
# Create BayesNet
self.bn = BayesianNetwork()
# Create Nodes
weather0 = DiscreteNode("Weather0", ["Sun", "Rain"])
weather = DiscreteNode("Weather", ["Sun", "Rain"])
ice_cream_eaten = DiscreteNode("Ice Cream Eaten", [True, False])
# Add nodes
self.bn.add_node(weather0)
self.bn.add_node(weather)
self.bn.add_node(ice_cream_eaten)
# Add edges
self.bn.add_edge(weather, ice_cream_eaten)
self.bn.add_edge(weather0, weather)
# Set probabilities
cpt_weather0 = numpy.array([.6, .4])
weather0.set_probability_table(cpt_weather0, [weather0])
cpt_weather = numpy.array([[.7, .5], [.3, .5]])
weather.set_probability_table(cpt_weather, [weather0, weather])
ice_cream_eaten.set_probability(.9, [(ice_cream_eaten, True),
(weather, "Sun")])
ice_cream_eaten.set_probability(.1, [(ice_cream_eaten, False),
(weather, "Sun")])
ice_cream_eaten.set_probability(.2, [(ice_cream_eaten, True),
(weather, "Rain")])
ice_cream_eaten.set_probability(.8, [(ice_cream_eaten, False),
(weather, "Rain")])
def test_import_export(self):
# write BN
xmlbif = XMLBIF(self.bn, "Test Net")
xmlbif.write("test_out.xmlbif")
# read BN
bn2 = XMLBIF.read("test_out.xmlbif")
for node1 in self.bn.get_nodes():
name_found = False
cpd_equal = False
value_range_equal = False
str_equal = False
pos_equal = False
for node2 in bn2.get_nodes():
print(node2.name)
print(node2.get_cpd())
# Test node names
print(node2.name)
if node1.name == node2.name:
name_found = True
cpd_equal = node1.get_cpd() == node2.get_cpd()
value_range_equal = node1.get_value_range(
) == node2.get_value_range()
str_equal = str(node1) == str(node2)
pos_equal = node1.pos == node2.pos
self.assertTrue(name_found)
self.assertTrue(cpd_equal)
self.assertTrue(value_range_equal)
self.assertTrue(str_equal)
self.assertTrue(pos_equal)
# remove file
os.remove("test_out.xmlbif")
class ImportExportTest(unittest.TestCase):
def setUp(self):
# Create BayesNet
self.bn = BayesianNetwork();
# Create Nodes
weather0 = DiscreteNode("Weather0", ["Sun", "Rain"])
weather = DiscreteNode("Weather", ["Sun", "Rain"])
ice_cream_eaten = DiscreteNode("Ice Cream Eaten", [True, False])
# Add nodes
self.bn.add_node(weather0)
self.bn.add_node(weather)
self.bn.add_node(ice_cream_eaten)
# Add edges
self.bn.add_edge(weather, ice_cream_eaten)
self.bn.add_edge(weather0, weather);
# Set probabilities
cpt_weather0 = numpy.array([.6, .4])
weather0.set_probability_table(cpt_weather0, [weather0])
cpt_weather = numpy.array([[.7, .5],
[.3, .5]])
weather.set_probability_table(cpt_weather, [weather0, weather])
ice_cream_eaten.set_probability(.9, [(ice_cream_eaten, True), (weather, "Sun")])
ice_cream_eaten.set_probability(.1, [(ice_cream_eaten, False), (weather, "Sun")])
ice_cream_eaten.set_probability(.2, [(ice_cream_eaten, True), (weather, "Rain")])
ice_cream_eaten.set_probability(.8, [(ice_cream_eaten, False), (weather, "Rain")])
def test_import_export(self):
# write BN
xmlbif = XMLBIF(self.bn, "Test Net")
xmlbif.write("test_out.xmlbif")
# read BN
bn2 = XMLBIF.read("test_out.xmlbif")
for node1 in self.bn.get_nodes():
name_found = False
cpd_equal = False
value_range_equal = False
str_equal = False
pos_equal = False
for node2 in bn2.get_nodes():
print(node2.name)
print(node2.get_cpd())
# Test node names
print(node2.name)
if node1.name == node2.name:
name_found = True
cpd_equal = node1.get_cpd() == node2.get_cpd()
value_range_equal = node1.get_value_range() == node2.get_value_range()
str_equal = str(node1) == str(node2)
pos_equal = node1.pos == node2.pos
self.assertTrue(name_found)
self.assertTrue(cpd_equal)
self.assertTrue(value_range_equal)
self.assertTrue(str_equal)
self.assertTrue(pos_equal)
# remove file
os.remove("test_out.xmlbif")
#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,{})
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
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from primo.networks import BayesianNetwork
from primo.nodes import DiscreteNode
import numpy
bn = BayesianNetwork()
burglary = DiscreteNode("Burglary", ["Intruder","Safe"])
alarm = DiscreteNode("Alarm", ["Ringing", "Silent","Kaputt"])
earthquake = DiscreteNode("Earthquake", ["Shaking", "Calm"])
john_calls = DiscreteNode("John calls", ["Calling", "Not Calling"])
mary_calls = DiscreteNode("Mary calls", ["Calling", "Not Calling"])
bn.add_node(burglary)
bn.add_node(alarm)
bn.add_node(earthquake)
bn.add_node(john_calls)
bn.add_node(mary_calls)
bn.add_edge(burglary,alarm)
bn.add_edge(earthquake, alarm)
bn.add_edge(alarm, john_calls)
bn.add_edge(alarm, mary_calls)
burglary.set_probability(0.2,[(burglary,"Intruder")])
alarm.set_probability(0.1,[(alarm,"Ringing"),(burglary,"Safe"),(earthquake,"Calm")])
from primo.networks import BayesianNetwork
from primo.nodes import DiscreteNode
#initialize a new BayesNet
bn = BayesianNetwork()
#create Nodes with Name and the possible values
burglary = DiscreteNode("Burglary", ["Intruder","Safe"])
alarm = DiscreteNode("Alarm", ["Ringing", "Silent"])
earthquake = DiscreteNode("Earthquake", ["Shaking", "Calm"])
john_calls = DiscreteNode("John calls", ["Calling", "Not Calling"])
baum_calls = DiscreteNode("Baum calls", ["Calling", "Not Calling"])
# add Nodes to BayesNet
bn.add_node(burglary)
bn.add_node(alarm)
bn.add_node(earthquake)
bn.add_node(john_calls)
bn.add_node(baum_calls)
# Add edges to show dependencies
bn.add_edge(burglary,alarm)
bn.add_edge(earthquake, alarm)
bn.add_edge(alarm, john_calls)
bn.add_edge(alarm, baum_calls)
#create probability tables and set them in the node
cpt_burglary = numpy.array([0.001,0.999])
burglary.set_probability_table(cpt_burglary,[burglary])
#About this example:
#This example shows how approximate inference can be used query a purely discrete
#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
bn = BayesianNetwork()
burglary = DiscreteNode("Burglary", ["Intruder", "Safe"])
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
# Construct a DynmaicBayesianNetwork
dbn = DynamicBayesianNetwork()
B0 = BayesianNetwork()
twoTBN = TwoTBN(XMLBIF.read("Robot_Localization.xmlbif"))
# Configure TwoTBN
x0 = twoTBN.get_node("x0")
x = twoTBN.get_node("x")
door = twoTBN.get_node("door")
twoTBN.set_initial_node(x0.name, x.name)
# Configure initial distribution
x0_init = DiscreteNode(
x0.name, ["p0", "p1", "p2", "p3", "p4", "p5", "p6", "p7", "p8", "p9"])
B0.add_node(x0_init)
cpt_x0_init = numpy.array([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1])
x0_init.set_probability_table(cpt_x0_init, [x0_init])
dbn.B0 = B0
dbn.twoTBN = twoTBN
N = 1000
T = 10
pos = 0
lastPos = 0
evidence = {}
def get_evidence_function():
class NodeAddAndRemoveTestCase(unittest.TestCase):
def setUp(self):
self.bn = BayesianNetwork()
def tearDown(self):
self.bn = None
def test_clear_and_len(self):
self.assertFalse(0 == len(self.bn))
self.assertFalse(0 == self.bn.number_of_nodes())
self.bn.clear()
self.assertEqual(0, len(self.bn))
self.assertEqual(0, self.bn.number_of_nodes())
def test_add_node(self):
self.bn.clear()
n = DiscreteNode("Some Node", [True, False])
self.bn.add_node(n)
self.assertEqual(n, self.bn.get_node("Some Node"))
self.assertTrue(n in self.bn.get_nodes(["Some Node"]))
node_with_same_name = DiscreteNode("Some Node", [True, False])
self.assertRaises(Exception, self.bn.add_node, node_with_same_name)
def test_remove_node(self):
self.bn.clear()
n = DiscreteNode("Some Node to remove", [True, False])
self.bn.add_node(n)
self.bn.remove_node(n)
self.assertFalse(n in self.bn.get_nodes([]))
def test_add_edge(self):
self.bn.clear()
n1 = DiscreteNode("1", [True, False])
n2 = DiscreteNode("2", [True, False])
self.bn.add_node(n1)
self.bn.add_node(n2)
self.bn.add_edge(n1, n2)
self.assertTrue(n1 in self.bn.get_parents(n2))
self.assertTrue(n2 in self.bn.get_children(n1))
def test_remove_edge(self):
self.bn.clear()
n1 = DiscreteNode("1", [True, False])
n2 = DiscreteNode("2", [True, False])
self.bn.add_node(n1)
self.bn.add_node(n2)
self.bn.add_edge(n1, n2)
self.assertEqual([n1], self.bn.get_parents(n2))
self.bn.remove_edge(n1, n2)
self.assertEqual([], self.bn.get_parents(n2))
def test_is_valid(self):
self.bn.clear()
n1 = DiscreteNode("1", [True, False])
n2 = DiscreteNode("2", [True, False])
self.bn.add_node(n1)
self.bn.add_node(n2)
self.bn.add_edge(n1, n2)
self.assertTrue(self.bn.is_valid())
self.bn.add_edge(n1, n1)
self.assertFalse(self.bn.is_valid())
self.bn.remove_edge(n1, n1)
self.assertTrue(self.bn.is_valid())
n3 = DiscreteNode("3", [True, False])
n4 = DiscreteNode("4", [True, False])
self.bn.add_node(n3)
self.bn.add_node(n4)
self.assertTrue(self.bn.is_valid())
self.bn.add_edge(n2, n3)
self.assertTrue(self.bn.is_valid())
self.bn.add_edge(n3, n4)
self.assertTrue(self.bn.is_valid())
self.bn.add_edge(n4, n1)
self.assertFalse(self.bn.is_valid())
#About this example:
#This example shows how approximate inference can be used query a purely discrete
#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
bn = BayesianNetwork()
burglary = DiscreteNode("Burglary", ["Intruder","Safe"])
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())
