gluonts.mx.representation.global_relative_binning module¶
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class
gluonts.mx.representation.global_relative_binning.
GlobalRelativeBinning
(num_bins: int = 1024, is_quantile: bool = True, linear_scaling_limit: int = 10, quantile_scaling_limit: float = 0.99, *args, **kwargs)[source]¶ Bases:
gluonts.mx.representation.representation.Representation
A class representing a global relative binning approach. This binning first rescales all input series by their respective mean (relative) and then performs one binning across all series (global).
- Parameters
num_bins – The number of discrete bins/buckets that we want values to be mapped to. (default: 1024)
is_quantile – Whether the binning is quantile or linear. Quantile binning allocated bins based on the cumulative distribution function, while linear binning allocates evenly spaced bins. (default: True, i.e. quantile binning)
linear_scaling_limit – The linear scaling limit. Values which are larger than linear_scaling_limit times the mean will be capped at linear_scaling_limit. (default: 10)
quantile_scaling_limit – The quantile scaling limit. Values which are larger than the quantile evaluated at quantile_scaling_limit will be capped at the quantile evaluated at quantile_scaling_limit. (default: 0.99)
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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]]
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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.
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initialize_from_dataset
(input_dataset: Iterable[Dict[str, Any]], ctx: mxnet.context.Context = cpu(0))[source]¶ Initialize the representation based on an entire dataset.
- Parameters
input_dataset – GluonTS dataset.
ctx – MXNet context.
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post_transform
(F, samples: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], scale: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], rep_params: List[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]¶ Transform samples back to the original representation.
- Parameters
samples – Samples from a distribution.
scale – The scale of the samples.
rep_params – Additional representation-specific parameters used during post transformation.
- Returns
Post-transformed samples.
- Return type
Tensor