Table Of Contents
Table Of Contents

gluonts.distribution.binned module

class gluonts.distribution.binned.Binned(bin_probs: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], bin_centers: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], F=None)[source]

Bases: gluonts.distribution.distribution.Distribution

A binned distribution defined by a set of bins via bin centers and bin probabilities.

Parameters:
  • bin_probs – Tensor containing the bin probabilities, of shape (*batch_shape, num_bins).
  • bin_centers – Tensor containing the bin centers, of shape (*batch_shape, num_bins).
  • 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.

cdf(x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]

Returns the value of the cumulative distribution function evaluated at x

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)[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
mean

Tensor containing the mean of the distribution.

quantile(level: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]

Calculates quantiles for the given levels.

Parameters:level – Level values to use for computing the quantiles. level should be a 1d tensor of level values between 0 and 1.
Returns:Quantile values corresponding to the levels passed. The return shape is
(num_levels, …DISTRIBUTION_SHAPE…),

where DISTRIBUTION_SHAPE is the shape of the underlying distribution.

Return type:quantiles
sample(num_samples=None, dtype=<class 'numpy.float32'>)[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

stddev

Tensor containing the standard deviation of the distribution.

class gluonts.distribution.binned.BinnedArgs(num_bins: int, bin_centers: mxnet.ndarray.ndarray.NDArray, **kwargs)[source]

Bases: mxnet.gluon.block.HybridBlock

hybrid_forward(F, x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], bin_centers: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Tuple[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]][source]

Overrides to construct symbolic graph for this Block.

Parameters:
  • x (Symbol or NDArray) – The first input tensor.
  • *args (list of Symbol or list of NDArray) – Additional input tensors.
class gluonts.distribution.binned.BinnedOutput(bin_centers: mxnet.ndarray.ndarray.NDArray)[source]

Bases: gluonts.distribution.distribution_output.DistributionOutput

distr_cls

alias of Binned

distribution(args, scale=None) → gluonts.distribution.binned.Binned[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.
event_shape

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

get_args_proj(*args, **kwargs) → mxnet.gluon.block.HybridBlock[source]