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src/p/y/pymc-2.3.2/pymc/distributions.py   pymc(Download)
                random = debug_wrapper(random)
            else:
                Stochastic.__init__(self, logp=logp, random=random, logp_partial_gradients = logp_partial_gradients, dtype=dtype, **arg_dict_out)
 
    new_class.__name__ = name
 
        if isinstance(p, Dirichlet):
            Stochastic.__init__(self, logp=valuewrapper(mod_categorical_like),
                                doc='A Categorical random variable', name=name,
                                parents={'p':p}, random=bind_size(rmod_categor, self.size),
                                verbose=verbose, **kwds)
        else:
            Stochastic.__init__(self, logp=valuewrapper(categorical_like),
                                doc='A Categorical random variable', name=name,
                                parents={'p':p},
 
        if isinstance(p, Dirichlet):
            Stochastic.__init__(self, logp=valuewrapper(mod_multinom_like),
                                doc='A Multinomial random variable', name=name,
                                parents={'n':n,'p':p}, random=mod_rmultinom,
                                verbose=verbose, **kwds)
        else:
            Stochastic.__init__(self, logp=valuewrapper(multinomial_like),
                                doc='A Multinomial random variable', name=name,
                                parents={'n':n,'p':p}, random=rmultinomial,

src/p/y/pymc-2.3.2/pymc/CircularStochastic.py   pymc(Download)
    def __init__(self, lo, hi, *args, **kwargs):
        self.interval_parents = Container([hi, lo])
        Stochastic.__init__(self, *args, **kwargs)
 
    def set_value(self, value):