def sample(self, pre_inst=None):
"""
Returns a random sample of the network in the form of an Event. An
event will contain a list of tuples. Each tuple is a pair between a
random variable and it's instance, in the form:
>>> ret = [(X, "T"), (Y, "F")]
:param pre_inst: Default=None. Should be a list of tuples. Each tuple
is a pair between a random variable and a value of
it's domain. That value will be the value in the
sample instead of leaving it to random chance.
"""
net = self.network[:]
inst_vars = Event()
for i in net:
insted = i.factor.instVar(inst_vars)
var = i.node
if pre_inst is not None:
val = pre_inst.value(i.node)
if pre_inst is None or val is None:
val = self.pickRandomValue(var.domain, insted.values,
random.random())
inst_vars.setValue(var, val)
return inst_vars
def sample(self, pre_inst=None):
"""
Returns a random sample of the network in the form of an Event. An
event will contain a list of tuples. Each tuple is a pair between a
random variable and it's instance, in the form:
>>> ret = [(X, "T"), (Y, "F")]
:param pre_inst: Default=None. Should be a list of tuples. Each tuple
is a pair between a random variable and a value of
it's domain. That value will be the value in the
sample instead of leaving it to random chance.
"""
net = self.network[:]
inst_vars = Event()
for i in net:
insted = i.factor.instVar(inst_vars)
var = i.node
if pre_inst is not None:
val = pre_inst.value(i.node)
if pre_inst is None or val is None:
val = self.pickRandomValue(var.domain, insted.values,
random.random())
inst_vars.setValue(var, val)
return inst_vars
def eventSetValue_test_6(self):
"""
Test setValue method
"""
event = Event(var=self.X, val=self.X.domain[0])
event.setValue(self.X, self.X.domain[1])
event.setValue(self.Y, self.Y.domain[1])
assert (len(event.event.keys()) == 2)
assert (event.value(self.X) == self.X.domain[1])
assert (event.value(self.Y) == self.Y.domain[1])
def eventSetValue_test_6(self):
"""
Test setValue method
"""
event = Event(var=self.X, val=self.X.domain[0])
event.setValue(self.X, self.X.domain[1])
event.setValue(self.Y, self.Y.domain[1])
assert(len(event.event.keys()) == 2)
assert(event.value(self.X) == self.X.domain[1])
assert(event.value(self.Y) == self.Y.domain[1])
# distribution. Note that the nodes were place in the list at random
network = [
(earthq, factor_earthq),
(alarm, factor_alarm),
(john, factor_john),
(burglary, factor_burglary),
(mary, factor_mary)
]
# The network object
BN = bn.BayesianNetwork(network)
# An event with the observations In this example the variable john was
# observed being true. Same for variable mary
observed = Event()
observed.setValue(john, "True")
observed.setValue(mary, "True")
# Run the elimination ask algorithm (Variable Elimination) for the example.
# Result should be approximately [0.284, 0.716]
burglary_k_john_mary = BN.eliminationAsk(burglary, observed)
print("P(Burglary | John=true, Mary=true)")
print(burglary_k_john_mary)
# Another example with the same network. The observation and query variable
# changed, but the order of the distributions is the same. The result
# should be approximately [0.849, 0.150]
observed = Event(var=burglary, val="True")
john_k_burglary = BN.eliminationAsk(john, observed)
