gluonts.mx.distribution.transformed_distribution module

class gluonts.mx.distribution.transformed_distribution.AffineTransformedDistribution(base_distribution: gluonts.mx.distribution.distribution.Distribution, loc: Optional[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]] = None, scale: Optional[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]] = None)

Bases: gluonts.mx.distribution.transformed_distribution.TransformedDistribution

arg_names: Tuple

property mean: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]

Tensor containing the mean of the distribution.

property stddev: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]

Tensor containing the standard deviation of the distribution.

property variance: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]

Tensor containing the variance of the distribution.

class gluonts.mx.distribution.transformed_distribution.TransformedDistribution(base_distribution: gluonts.mx.distribution.distribution.Distribution, transforms: List[gluonts.mx.distribution.bijection.Bijection])

Bases: gluonts.mx.distribution.distribution.Distribution

A distribution obtained by applying a sequence of transformations on top of a base distribution.

property F

arg_names: Tuple

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.

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

Return the value of the cumulative distribution function evaluated at x.

property 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.

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(y: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]

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

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

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: typing.Optional[int] = None, dtype=<class 'numpy.float32'>) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]

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

sample_rep(num_samples: typing.Optional[int] = None, dtype=<class 'float'>) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]

property support_min_max: Tuple[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]]

gluonts.mx.distribution.transformed_distribution.sum_trailing_axes(F, x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], k: int) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]