gluonts.mx.distribution.categorical module#

class gluonts.mx.distribution.categorical.Categorical(log_probs: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol])[source]#

Bases: gluonts.mx.distribution.distribution.Distribution

A categorical distribution over num_cats-many categories.

Parameters

  • log_probs – Tensor containing log probabilities of the individual categories, of shape (*batch_shape, num_cats).

  • 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.

log_prob(x)[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#

Tensor containing the mean of the distribution.

property probs#
sample(num_samples=None, dtype=<class 'numpy.int32'>)[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#

Tensor containing the standard deviation of the distribution.

class gluonts.mx.distribution.categorical.CategoricalOutput(num_cats: int, temperature: float = 1.0)[source]#

Bases: gluonts.mx.distribution.distribution_output.DistributionOutput

distr_cls#

alias of gluonts.mx.distribution.categorical.Categorical

distribution(distr_args, loc=None, scale=None, **kwargs) → gluonts.mx.distribution.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.

domain_map(F, probs)[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.