gluonts.mx.representation package¶
-
class
gluonts.mx.representation.
Representation
[source]¶ Bases:
mxnet.gluon.block.HybridBlock
An abstract class representing input/output representations.
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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
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class
gluonts.mx.representation.
CustomBinning
(bin_centers: numpy.ndarray, *args, **kwargs)[source]¶ Bases:
gluonts.mx.representation.representation.Representation
A class representing binned representations with custom centers.
- Parameters
bin_centers – The bins to be used to discretize the data. (default: 1024)
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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
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class
gluonts.mx.representation.
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
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class
gluonts.mx.representation.
LocalAbsoluteBinning
(num_bins: int = 1024, is_quantile: bool = True, *args, **kwargs)[source]¶ Bases:
gluonts.mx.representation.representation.Representation
A class representing a local absolute binning approach. This binning estimates a binning for every single time series on a local level and therefore implicitly acts as a scaling mechanism.
- 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)
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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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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
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class
gluonts.mx.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.
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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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class
gluonts.mx.representation.
MeanScaling
(scale_min: float = 1e-10, clip_max: Optional[float] = None, *args, **kwargs)[source]¶ Bases:
gluonts.mx.representation.representation.Representation
A class representing a mean scaling approach. Inputs are simply rescaled based on their mean.
- Parameters
minimum_scale – The minimum value to which re-scaled values will be clipped to. (default: 1e-10)
clip_max – The maximum value to which re-scaled values will be clipped to. Negative values will be clipped at -clip_max and positive values at clip_max. (default: None)
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compute_scale
(F, data: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], observed_indicator: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]¶
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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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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
-
class
gluonts.mx.representation.
DimExpansion
(axis: int = -1, *args, **kwargs)[source]¶ Bases:
gluonts.mx.representation.representation.Representation
A class representing a dimension expansion operation along a specified axis.
- Parameters
axis – Axis on which to expand the tensor. (default: -1)
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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]]
-
class
gluonts.mx.representation.
Embedding
(num_bins: int, size: Optional[int] = None, *args, **kwargs)[source]¶ Bases:
gluonts.mx.representation.representation.Representation
A class representing an embedding operation on top of a given binning. Note that this representation is intended to applied on top of categorical/binned data.
- Parameters
num_bins – The number of categories/bins of the data on which this representation is applied.
size – The desired embedding size. By default, the following heuristic is used: https://developers.googleblog.com/2017/11/introducing-tensorflow-feature-columns.html (default: round(num_bins**(1/4)))
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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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class
gluonts.mx.representation.
DiscretePIT
(num_bins: int, mlp_transf: bool = False, embedding_size: Optional[int] = None, *args, **kwargs)[source]¶ Bases:
gluonts.mx.representation.representation.Representation
A class representing a discrete probability integral transform of a given quantile-based learned binning. Note that this representation is intended to be applied on top of a quantile-based binning representation.
- Parameters
num_bins – Number of bins used by the data on which this representation is applied.
mlp_tranf – Whether we want to post-process the pit-transformed valued using a MLP which can learn an appropriate binning, which would ensure that pit models have the same expressiveness as standard quantile binning with embedding. (default: False)
embedding_size – The desired layer output size if mlp_tranf=True. By default, the following heuristic is used: https://developers.googleblog.com/2017/11/introducing-tensorflow-feature-columns.html (default: round(num_bins**(1/4)))
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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]]
-
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
-
class
gluonts.mx.representation.
RepresentationChain
(chain: 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
chain – 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 = cpu(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 = cpu(0))[source]¶ Initialize the representation based on an entire dataset.
- Parameters
input_dataset – GluonTS dataset.
ctx – MXNet context.
-
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
Submodules¶
- gluonts.mx.representation.binning_helpers module
- gluonts.mx.representation.custom_binning module
- gluonts.mx.representation.dim_expansion module
- gluonts.mx.representation.discrete_pit module
- gluonts.mx.representation.embedding module
- gluonts.mx.representation.global_relative_binning module
- gluonts.mx.representation.hybrid_representation module
- gluonts.mx.representation.local_absolute_binning module
- gluonts.mx.representation.mean_scaling module
- gluonts.mx.representation.representation module
- gluonts.mx.representation.representation_chain module