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Delete BayesNet.variables(); add check that X is distinct from evidence.

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commit e9f77206287a10c25cd8aa374e70861fab6648a3 1 parent 9273ba7
@darius authored
Showing with 4 additions and 9 deletions.
  1. +4 −9
@@ -157,7 +157,7 @@ def add(self, node):
"""Add a node to the net. Its parents must already be in the
net, and node itself must not."""
assert node not in self.nodes
- assert every(lambda parent: parent in self.variables(), node.parents)
+ assert every(lambda parent: parent in self.vars, node.parents)
for parent in node.parents:
@@ -172,12 +172,6 @@ def variable_node(self, var):
return n
raise Exception("No such variable: %s" % var)
- def variables(self):
- """List all of the net's variables, parents before children.
- >>> burglary.variables()
- ['Burglary', 'Earthquake', 'Alarm', 'JohnCalls', 'MaryCalls']"""
- return [n.variable for n in self.nodes]
def variable_values(self, var):
"Return the domain of var."
return [True, False]
@@ -270,9 +264,10 @@ def enumeration_ask(X, e, bn):
>>> enumeration_ask('Burglary', dict(JohnCalls=T, MaryCalls=T), burglary
... ).show_approx()
'False: 0.716, True: 0.284'"""
+ assert X not in e, "Query variable must be distinct from evidence"
Q = ProbDist(X)
for xi in bn.variable_values(X):
- Q[xi] = enumerate_all(bn.variables(), extend(e, X, xi), bn)
+ Q[xi] = enumerate_all(bn.vars, extend(e, X, xi), bn)
return Q.normalize()
def enumerate_all(vars, e, bn):
@@ -398,7 +393,7 @@ def gibbs_ask(X, e, bn, N):
'False: 0.738, True: 0.262'
counts = dict((x, 0) for x in bn.variable_values(X)) # bold N in Fig. 14.16
- Z = [var for var in bn.variables() if var not in e]
+ Z = [var for var in bn.vars if var not in e]
state = dict(e) # boldface x in Fig. 14.16
for Zi in Z:
state[Zi] = choice(bn.variable_values(Zi))
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