Table Of Contents
Table Of Contents

# gluonts.distribution.neg_binomial module¶

class gluonts.distribution.neg_binomial.NegativeBinomial(mu: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], alpha: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], F=None)[source]

Negative binomial distribution, i.e. the distribution of the number of successes in a sequence of independet 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 –
args
batch_shape

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.

event_dim

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.

event_shape

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[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]

Compute the log-density of the distribution at x.

Parameters: x – Tensor of shape (*batch_shape, *event_shape). Tensor of shape batch_shape containing the log-density of the distribution for each event in x. Tensor
mean

Tensor containing the mean of the distribution.

sample(num_samples: Optional[int] = None, dtype=<class 'numpy.float32'>) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.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. A tensor containing samples. This has shape (*batch_shape, *eval_shape) if num_samples = None and (num_samples, *batch_shape, *eval_shape) otherwise. Tensor
stddev

Tensor containing the standard deviation of the distribution.

class gluonts.distribution.neg_binomial.NegativeBinomialOutput[source]
args_dim = {'alpha': 1, 'mu': 1}
distr_cls

alias of NegativeBinomial

distribution(distr_args, scale=None) → gluonts.distribution.neg_binomial.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. 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.

event_shape

Shape of each individual event contemplated by the distributions that this object constructs.