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_complicated_multi(self):
n1 = DiscreteNode("Some Node", [True, False])
n2 = DiscreteNode("Second Node" , [True, False,"noIdea"])
cpt1 = numpy.array([2,3])
cpt2 = numpy.array([5,7,9])
n1.set_probability_table(cpt1,[n1])
n2.set_probability_table(cpt2,[n2])
c3 = n1.get_cpd().multiplication(n2.get_cpd())
c3 = n1.get_cpd().multiplication(c3)
cptN = numpy.array([[20, 28, 36],[45, 63, 81]])
numpy.testing.assert_array_equal(c3.table,cptN)
def test_complicated_multi(self):
n1 = DiscreteNode("Some Node", [True, False])
n2 = DiscreteNode("Second Node", [True, False, "noIdea"])
cpt1 = numpy.array([2, 3])
cpt2 = numpy.array([5, 7, 9])
n1.set_probability_table(cpt1, [n1])
n2.set_probability_table(cpt2, [n2])
c3 = n1.get_cpd().multiplication(n2.get_cpd())
c3 = n1.get_cpd().multiplication(c3)
cptN = numpy.array([[20, 28, 36], [45, 63, 81]])
numpy.testing.assert_array_equal(c3.table, cptN)
def test_easy_values(self):
n1 = DiscreteNode("Some Node", [True, False])
n2 = DiscreteNode("Second Node" , [True, False])
cpt1 = numpy.array([2,3])
cpt2 = numpy.array([5,7])
n1.set_probability_table(cpt1,[n1])
n2.set_probability_table(cpt2,[n2])
s = n1.get_cpd().multiplication(n2.get_cpd())
cptN = numpy.array([[10,14],[15,21]])
numpy.testing.assert_array_equal(s.table,cptN)
self.assertEqual(s.variables[0],n1)
def test_easy_values(self):
n1 = DiscreteNode("Some Node", [True, False])
n2 = DiscreteNode("Second Node", [True, False])
cpt1 = numpy.array([2, 3])
cpt2 = numpy.array([5, 7])
n1.set_probability_table(cpt1, [n1])
n2.set_probability_table(cpt2, [n2])
s = n1.get_cpd().multiplication(n2.get_cpd())
cptN = numpy.array([[10, 14], [15, 21]])
numpy.testing.assert_array_equal(s.table, cptN)
self.assertEqual(s.variables[0], n1)
def test_easy_marginalize(self):
n1 = DiscreteNode("Some Node", [True, False])
n2 = DiscreteNode("Second Node" , [True, False, "other"])
cpt1 = numpy.array([2,3])
cpt2 = numpy.array([5,7,3])
n1.set_probability_table(cpt1,[n1])
n2.set_probability_table(cpt2,[n2])
s = n1.get_cpd().multiplication(n2.get_cpd())
s =s.marginalization(n2)
print s.table
cptN = numpy.array([30,45])
numpy.testing.assert_array_equal(s.table,cptN)
self.assertEqual(s.variables[0],n1)
def test_easy_marginalize(self):
n1 = DiscreteNode("Some Node", [True, False])
n2 = DiscreteNode("Second Node", [True, False, "other"])
cpt1 = numpy.array([2, 3])
cpt2 = numpy.array([5, 7, 3])
n1.set_probability_table(cpt1, [n1])
n2.set_probability_table(cpt2, [n2])
s = n1.get_cpd().multiplication(n2.get_cpd())
s = s.marginalization(n2)
print s.table
cptN = numpy.array([30, 45])
numpy.testing.assert_array_equal(s.table, cptN)
self.assertEqual(s.variables[0], n1)
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([0.6, 0.4])
weather0.set_probability_table(cpt_weather0, [weather0])
cpt_weather = numpy.array([[0.7, 0.5], [0.3, 0.5]])
weather.set_probability_table(cpt_weather, [weather0, weather])
ice_cream_eaten.set_probability(0.9, [(ice_cream_eaten, True), (weather, "Sun")])
ice_cream_eaten.set_probability(0.1, [(ice_cream_eaten, False), (weather, "Sun")])
ice_cream_eaten.set_probability(0.2, [(ice_cream_eaten, True), (weather, "Rain")])
ice_cream_eaten.set_probability(0.8, [(ice_cream_eaten, False), (weather, "Rain")])
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")])
cpt = numpy.array([[0.1,0.9],[0.5,0.5],[0.4,0.6]])
john_calls.set_probability_table(cpt, [alarm, john_calls])
print john_calls.is_valid()
print alarm.is_valid()
bn.draw()
# 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]) cpt_earthquake = numpy.array([0.002,0.998]) earthquake.set_probability_table(cpt_earthquake,[earthquake]) #another possibility to set probabilities alarm.set_probability(0.95,[(alarm,"Ringing"),(burglary,"Intruder"),(earthquake,"Shaking")]) alarm.set_probability(0.05,[(alarm,"Silent"),(burglary,"Intruder"),(earthquake,"Shaking")]) alarm.set_probability(0.29,[(alarm,"Ringing"),(burglary,"Safe"),(earthquake,"Shaking")]) alarm.set_probability(0.71,[(alarm,"Silent"),(burglary,"Safe"),(earthquake,"Shaking")]) alarm.set_probability(0.94,[(alarm,"Ringing"),(burglary,"Intruder"),(earthquake,"Calm")]) alarm.set_probability(0.06,[(alarm,"Silent"),(burglary,"Intruder"),(earthquake,"Calm")]) alarm.set_probability(0.001,[(alarm,"Ringing"),(burglary,"Safe"),(earthquake,"Calm")]) alarm.set_probability(0.999,[(alarm,"Silent"),(burglary,"Safe"),(earthquake,"Calm")]) baum_calls.set_probability(0.9,[(alarm,"Ringing"),(baum_calls,"Calling")])
#-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
evidence = {burglary: EvEq("Intruder")}
print "-------ProbabilityOfEvidence:-------"
poe = mcmc_ask.calculate_PoE(evidence)
print "p(evidence=Intruder)=" + str(poe)
print "Ground truth=0.2\n"
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")])
cpt = numpy.array([[0.1, 0.9], [0.5, 0.5], [0.4, 0.6]])
john_calls.set_probability_table(cpt, [alarm, john_calls])
print john_calls.is_valid()
print alarm.is_valid()
bn.draw()
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():
global pos
global 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
evidence={burglary:EvEq("Intruder")}
print "-------ProbabilityOfEvidence:-------"
poe=mcmc_ask.calculate_PoE(evidence)
print "p(evidence=Intruder)="+str(poe)
print "Ground truth=0.2\n"
