gluonts.mx.distribution.deterministic module#

class gluonts.mx.distribution.deterministic.Deterministic(value: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol])[source]#

Bases: gluonts.mx.distribution.distribution.Distribution

Deterministic/Degenerate distribution. :param value: Tensor containing the values, of shape (*batch_shape, *event_shape). :param 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.

cdf(x)[source]#: Return the value of the cumulative distribution function evaluated at x.

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 = True#

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).
Returns: Tensor of shape batch_shape containing the log-density of the distribution for each event in x.
Return type: Tensor

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

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[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]#: Tensor containing the standard deviation of the distribution.

class gluonts.mx.distribution.deterministic.DeterministicArgProj(value: float, args_dim: typing.Dict[str, int], dtype: typing.Type = <class 'numpy.float32'>, **kwargs)[source]#

Bases: mxnet.gluon.block.HybridBlock

hybrid_forward(F, x: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Tuple[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.mx.distribution.deterministic.DeterministicOutput(value: float)[source]#

Bases: gluonts.mx.distribution.distribution_output.DistributionOutput

args_dim: Dict[str, int] = {'value': 1}#

distr_cls#: alias of gluonts.mx.distribution.deterministic.Deterministic

property event_shape: Tuple#: Shape of each individual event contemplated by the distributions that this object constructs.

get_args_proj(prefix: Optional[str] = None) → mxnet.gluon.block.HybridBlock[source]#