gluonts.mx.distribution.neg_binomial module#

class gluonts.mx.distribution.neg_binomial.NegativeBinomial(mu: Union[NDArray, Symbol], alpha: Union[NDArray, Symbol])[source]#

Bases: Distribution

Negative binomial distribution, i.e. the distribution of the number of successes in a sequence of independent Bernoulli trials.

Parameters:

  • mu – Tensor containing the means, of shape (*batch_shape, *event_shape).

  • alpha – Tensor of the shape parameters, of shape (*batch_shape, *event_shape).

  • F –

property F#
arg_names: Tuple#
property args: List#
property batch_shape: Tuple#

Layout of the set of events contemplated by the distribution.

Invoking sample() from a distribution yields a tensor of shape batch_shape + event_shape, and computing log_prob (or loss more in general) on such sample will yield a tensor of shape batch_shape.

This property is available in general only in mx.ndarray mode, when the shape of the distribution arguments can be accessed.

property event_dim: int#

Number of event dimensions, i.e., length of the event_shape tuple.

This is 0 for distributions over scalars, 1 over vectors, 2 over matrices, and so on.

property event_shape: Tuple#

Shape of each individual event contemplated by the distribution.

For example, distributions over scalars have event_shape = (), over vectors have event_shape = (d, ) where d is the length of the vectors, over matrices have event_shape = (d1, d2), and so on.

Invoking sample() from a distribution yields a tensor of shape batch_shape + event_shape.

This property is available in general only in mx.ndarray mode, when the shape of the distribution arguments can be accessed.

is_reparameterizable = False#
log_prob(x: Union[NDArray, Symbol]) → Union[NDArray, Symbol][source]#

Compute the log-density of the distribution at x.

Parameters:

x – Tensor of shape (*batch_shape, *event_shape).

Returns:

Tensor of shape batch_shape containing the log-density of the distribution for each event in x.

Return type:

Tensor

property mean: Union[NDArray, Symbol]#

Tensor containing the mean of the distribution.

sample(num_samples: ~typing.Optional[int] = None, dtype=<class 'numpy.float32'>) → Union[NDArray, Symbol][source]#

Draw samples from the distribution.

If num_samples is given the first dimension of the output will be num_samples.

Parameters:

  • num_samples – Number of samples to to be drawn.

  • dtype – Data-type of the samples.

Returns:

A tensor containing samples. This has shape (*batch_shape, *eval_shape) if num_samples = None and (num_samples, *batch_shape, *eval_shape) otherwise.

Return type:

Tensor

property stddev: Union[NDArray, Symbol]#

Tensor containing the standard deviation of the distribution.

class gluonts.mx.distribution.neg_binomial.NegativeBinomialOutput[source]#

Bases: DistributionOutput

args_dim: Dict[str, int] = {'alpha': 1, 'mu': 1}#
distr_cls#

alias of NegativeBinomial

distribution(distr_args, loc: Optional[Union[NDArray, Symbol]] = None, scale: Optional[Union[NDArray, Symbol]] = None) → NegativeBinomial[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(F, mu, alpha)[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 contemplated by the distributions that this object constructs.

gluonts.mx.distribution.neg_binomial.ZeroInflatedNegativeBinomialOutput() → MixtureDistributionOutput[source]#