def fold_normal(self, name, mean=0., _sentinel=None, std=None, logstd=None, n_samples=None, group_ndims=0, is_reparameterized=True, check_numerics=False, **kwargs): """ Add a stochastic node in this :class:`BayesianNet` that follows the FoldNormal distribution. :param name: The name of the stochastic node. Must be unique in a :class:`BayesianNet`. See :class:`~zhusuan.distributions.univariate.FoldNormal` for more information about the other arguments. :return: A :class:`StochasticTensor` instance. """ dist = distributions.FoldNormal(mean, _sentinel=_sentinel, std=std, logstd=logstd, group_ndims=group_ndims, is_reparameterized=is_reparameterized, check_numerics=check_numerics, **kwargs) return self.stochastic(name, dist, n_samples=n_samples, **kwargs)
def __init__(self, name, mean=0., logstd=None, std=None, n_samples=None, group_ndims=0, is_reparameterized=True, check_numerics=False, **kwargs): norm = distributions.FoldNormal(mean, logstd=logstd, std=std, group_ndims=group_ndims, is_reparameterized=is_reparameterized, check_numerics=check_numerics, **kwargs) super(FoldNormal, self).__init__(name, norm, n_samples)