def __init__(self,
n,
p=None,
logit_p=None,
name="Binomial",
learnable=False):
self._type = "Binomial"
if p is not None and logit_p is None:
ranges = {
"n": geometric_ranges.UnboundedRange(),
"p": geometric_ranges.Interval(0., 1.)
}
super().__init__(name,
n=n,
p=p,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.BinomialDistribution()
elif logit_p is not None and p is None:
ranges = {
"n": geometric_ranges.UnboundedRange(),
"logit_p": geometric_ranges.UnboundedRange()
}
super().__init__(name,
n=n,
logit_p=logit_p,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.BinomialDistribution()
else:
raise ValueError("Either p or " +
"logit_p needs to be provided as input")
def __init__(self,
probs=None,
logits=None,
name="Bernulli",
learnable=False,
is_observed=False):
self._type = "Bernulli"
if probs is not None and logits is None:
ranges = {"probs": geometric_ranges.Interval(0., 1.)}
super().__init__(name,
probs=probs,
learnable=learnable,
ranges=ranges,
is_observed=is_observed)
self.distribution = distributions.BernulliDistribution()
elif logits is not None and probs is None:
ranges = {"logits": geometric_ranges.UnboundedRange()}
super().__init__(name,
logits=logits,
learnable=learnable,
ranges=ranges)
self.distribution = distributions.BernulliDistribution()
else:
raise ValueError("Either probs or " +
"logits needs to be provided as input")
def __init__(self, total_count, probs=None, logits=None, name="NegativeBinomial", learnable=False, is_observed=False):
self._type = "NegativeBinomial"
if probs is not None and logits is None:
ranges = {"total_count": geometric_ranges.UnboundedRange(), #TODO: It should become natural number in the future
"probs": geometric_ranges.Interval(0., 1.)}
super().__init__(name, total_count=total_count, probs=probs, learnable=learnable, ranges=ranges, is_observed=is_observed)
self.distribution = distributions.BinomialDistribution()
elif logits is not None and probs is None:
ranges = {"total_count": geometric_ranges.UnboundedRange(),
"logits": geometric_ranges.UnboundedRange()}
super().__init__(name, total_count=total_count, logits=logits, learnable=learnable, ranges=ranges)
self.distribution = distributions.NegativeBinomialDistribution()
else:
raise ValueError("Either probs or " +
"logits needs to be provided as input")