@@ -201,23 +201,23 @@ def __init__(self, distribution, lower=None, upper=None):
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self .lower = lower
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self .upper = upper
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- def __call__ (self , * args , ** kwargs ):
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+ def __call__ (self , name , * args , ** kwargs ):
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if 'observed' in kwargs :
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raise ValueError ('Observed Bound distributions are not supported. '
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'If you want to model truncated data '
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'you can use a pm.Potential in combination '
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'with the cumulative probability function. See '
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'pymc3/examples/censored_data.py for an example.' )
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- first , args = args [0 ], args [1 :]
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if issubclass (self .distribution , Continuous ):
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- return _ContinuousBounded (first , self .distribution ,
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+ return _ContinuousBounded (name , self .distribution ,
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self .lower , self .upper , * args , ** kwargs )
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elif issubclass (self .distribution , Discrete ):
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- return _DiscreteBounded (first , self .distribution ,
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+ return _DiscreteBounded (name , self .distribution ,
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self .lower , self .upper , * args , ** kwargs )
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else :
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- raise ValueError ('Distribution is neither continuous nor discrete.' )
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+ raise ValueError (
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+ 'Distribution is neither continuous nor discrete.' )
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def dist (self , * args , ** kwargs ):
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if issubclass (self .distribution , Continuous ):
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