gluonts.mx.representation.hybrid_representation module

class gluonts.mx.representation.hybrid_representation.HybridRepresentation(representations: List, *args, **kwargs)[source]

Bases: gluonts.mx.representation.representation.Representation

A class representing a hybrid approach of combining multiple representations into a single representation. Representations will be combined by concatenating them on dim=-1.

Parameters

representations – A list of representations. Elements must be of type Representation.

hybrid_forward(F, data: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], observed_indicator: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], scale: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol, None], rep_params: List[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]], **kwargs) → Tuple[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], List[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]]][source]

Transform the data into the desired representation.

Parameters

  • F –

  • data – Target data.

  • observed_indicator – Target observed indicator.

  • scale – Pre-computed scale.

  • rep_params – Additional pre-computed representation parameters.

  • **kwargs, – Additional block-specfic parameters.

Returns

Tuple consisting of the transformed data, the computed scale, and additional parameters to be passed to post_transform.

Return type

Tuple[Tensor, Tensor, List[Tensor]]

initialize_from_array(input_array: numpy.ndarray, ctx: mxnet.context.Context = gpu(0))[source]

Initialize the representation based on a numpy array.

Parameters

  • input_array – Numpy array.

  • ctx – MXNet context.

initialize_from_dataset(input_dataset: Iterable[Dict[str, Any]], ctx: mxnet.context.Context = gpu(0))[source]

Initialize the representation based on an entire dataset.

Parameters

  • input_dataset – GluonTS dataset.

  • ctx – MXNet context.