def __init__(self,
probs=None,
logits=None,
name="Categorical",
learnable=False,
is_observed=False):
self._type = "Categorical"
if probs is not None and logits is None:
ranges = {"p": geometric_ranges.Simplex()}
super().__init__(name,
probs=probs,
learnable=learnable,
ranges=ranges,
is_observed=is_observed)
self.distribution = distributions.CategoricalDistribution()
elif logits is not None and probs is None:
ranges = {"logits": geometric_ranges.UnboundedRange()}
super().__init__(name,
logits=logits,
learnable=learnable,
ranges=ranges,
is_observed=is_observed)
self.distribution = distributions.CategoricalDistribution()
else:
raise ValueError("Either probs or " +
"logits needs to be provided as input")
def __init__(self, tau, p, name, learnable=False):
self._type = "Concrete"
ranges = {
"tau": geometric_ranges.RightHalfLine(0.),
"p": geometric_ranges.Simplex()
}
super().__init__(name,
tau=tau,
p=p,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.ConcreteDistribution()
def __init__(self,
p=None,
softmax_p=None,
name="Categorical",
learnable=False):
self._type = "Categorical"
if p is not None and softmax_p is None:
ranges = {"p": geometric_ranges.Simplex()}
super().__init__(name, p=p, learnable=learnable, ranges=ranges)
self.distribution = distributions.CategoricalDistribution()
elif softmax_p is not None and p is None:
ranges = {"softmax_p": geometric_ranges.UnboundedRange()}
super().__init__(name,
softmax_p=softmax_p,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.CategoricalDistribution()
else:
raise ValueError("Either p or " +
"softmax_p needs to be provided as input")
