39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
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class BayesNet:
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def __init__(self, ldep=None): # Why not ldep={}? See footnote 1.
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if not ldep:
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ldep = {}
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self.dependencies = ldep
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# The network data is stored in a dictionary that
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# associates the dependencies to each variable:
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# { v1:deps1, v2:deps2, ... }
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# These dependencies are themselves given
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# by another dictionary that associates conditional
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# probabilities to conjunctions of mother variables:
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# { mothers1:cp1, mothers2:cp2, ... }
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# The conjunctions are frozensets of pairs (mothervar,boolvalue)
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def add(self,var,mothers,prob):
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self.dependencies.setdefault(var,{})[frozenset(mothers)] = prob
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# Joint probability for a given conjunction of
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# all variables of the network
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def jointProb(self,conjunction):
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prob = 1.0
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for (var,val) in conjunction:
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for (mothers,p) in self.dependencies[var].items():
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if mothers.issubset(conjunction):
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prob*=(p if val else 1-p)
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return prob
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# Footnote 1:
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# Default arguments are evaluated on function definition,
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# not on function evaluation.
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# This creates surprising behaviour when the default argument is mutable.
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# See:
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# http://docs.python-guide.org/en/latest/writing/gotchas/#mutable-default-arguments
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