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: Optional[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]], 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.
:param : 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 = cpu(0))[source]#
Initialize the representation based on a numpy array.
- Parameters
input_array – Numpy array.
ctx – MXNet context.
- initialize_from_dataset(input_dataset: gluonts.dataset.Dataset, ctx: mxnet.context.Context = cpu(0))[source]#
Initialize the representation based on an entire dataset.
- Parameters
input_dataset – GluonTS dataset.
ctx – MXNet context.