def Earthquake(self, sample):
if sample['Alarm']:
if sample['Burglary']:
return probability(0.0020212)
else:
return probability(0.36755)
else:
if sample['Burglary']:
return probability(0.0016672)
else:
return probability(0.0014222)
def Burglary(self, sample):
if sample['Alarm']:
if sample['Earthquake']:
return probability(0.00327)
else:
return probability(0.485)
else:
if sample['Earthquake']:
return probability(7.05e-05)
else:
return probability(6.01e-05)
def sample(self, event):
"""
Sample from the distribution for this variable conditioned
on event's values for parent_variables. That is, return True/False
at random according with the conditional probability given the
parents.
"""
return probability(self.p(True, event))
def markov_blanket_sample(X, e, bn):
"""
Return a sample from P(X | mb) where mb denotes that the
variables in the Markov blanket of X take their values from event
e (which must assign a value to each). The Markov blanket of X is
X's parents, children, and children's parents.
"""
Xnode = bn.variable_node(X)
Q = ProbDist(X)
for xi in bn.variable_values(X):
ei = extend(e, X, xi)
# [Equation 13.12:]
Q[xi] = Xnode.p(xi, e) * product(
Yj.p(ei[Yj.variable], ei) for Yj in Xnode.children)
# (assuming a Boolean variable here)
return probability(Q.normalize()[True])
def JongCalls(self, sample):
if sample['Alarm']:
return probability(0.9)
else:
return probability(0.05)
def MaryCalls(self, sample):
if sample['Alarm']:
return probability(0.7)
else:
return probability(0.01)
def feature_bagging(dataset, p=0.7):
"""Feature bagging with probability p to retain an attribute"""
inputs = [i for i in dataset.inputs if probability(p)]
return inputs or dataset.inputs
