gluonts.torch.distributions.negative_binomial module#
- class gluonts.torch.distributions.negative_binomial.NegativeBinomial(total_count: Union[float, torch.Tensor], probs: Optional[Union[float, torch.Tensor]] = None, logits: Optional[Union[float, torch.Tensor]] = None, validate_args=None)[source]#
Bases:
torch.distributions.negative_binomial.NegativeBinomial
Negative binomial distribution with total_count and probs or logits parameters.
Based on torch.distributions.NegativeBinomial, with added cdf and icdf methods.
- cdf(value: torch.Tensor) torch.Tensor [source]#
Returns the cumulative density/mass function evaluated at value.
- Parameters
value (Tensor) –
- icdf(value: torch.Tensor) torch.Tensor [source]#
Returns the inverse cumulative density/mass function evaluated at value.
- Parameters
value (Tensor) –
- property scipy_nbinom#
- class gluonts.torch.distributions.negative_binomial.NegativeBinomialOutput(beta: float = 0.0)[source]#
Bases:
gluonts.torch.distributions.distribution_output.DistributionOutput
- args_dim: Dict[str, int] = {'logits': 1, 'total_count': 1}#
- distr_cls#
alias of
gluonts.torch.distributions.negative_binomial.NegativeBinomial
- distribution(distr_args, loc: Optional[torch.Tensor] = None, scale: Optional[torch.Tensor] = None) torch.distributions.distribution.Distribution [source]#
Construct the associated distribution, given the collection of constructor arguments and, optionally, a scale tensor.
- Parameters
distr_args – Constructor arguments for the underlying Distribution type.
loc – Optional tensor, of the same shape as the batch_shape+event_shape of the resulting distribution.
scale – Optional tensor, of the same shape as the batch_shape+event_shape of the resulting distribution.
- classmethod domain_map(total_count: torch.Tensor, logits: torch.Tensor)[source]#
Converts arguments to the right shape and domain.
The domain depends on the type of distribution, while the correct shape is obtained by reshaping the trailing axis in such a way that the returned tensors define a distribution of the right event_shape.
- property event_shape: Tuple#
Shape of each individual event compatible with the output object.
- in_features: int#