gluonts.mx.block.scaler module#
- class gluonts.mx.block.scaler.MeanScaler(minimum_scale: float = 1e-10, default_scale: Optional[float] = None, *args, **kwargs)[source]#
Bases:
gluonts.mx.block.scaler.Scaler
The
MeanScaler
computes a per-item scale according to the average absolute value over time of each item. The average is computed only among the observed values in the data tensor, as indicated by the second argument. Items with no observed data are assigned a scale based on the global average.- Parameters
minimum_scale – default scale that is used if the time series has only zeros.
- 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]#
- Parameters
F – A module that can either refer to the Symbol API or the NDArray API in MXNet.
data – tensor containing the data to be scaled.
observed_indicator – observed_indicator: binary tensor with the same shape as
data
, that has 1 in correspondence of observed data points, and 0 in correspondence of missing data points.
- Returns
shape (N, C), computed according to the average absolute value over time of the observed values.
- Return type
Tensor
- class gluonts.mx.block.scaler.MinMax(*args, **kwargs)[source]#
Bases:
gluonts.mx.block.scaler.Scaler
The ‘MinMax’ scales the input data using a min-max approach along the specified axis.
- 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]#
- Parameters
F – A module that can either refer to the Symbol API or the NDArray API in MXNet.
data – tensor containing the data to be scaled.
observed_indicator – observed_indicator: binary tensor with the same shape as
data
, that has 1 in correspondence of observed data points, and 0 in correspondence of missing data points.
- Returns
shape (N, T, C) or (N, C, T) scaled along the specified axis.
- Return type
Tensor
- class gluonts.mx.block.scaler.NOPScaler(*args, **kwargs)[source]#
Bases:
gluonts.mx.block.scaler.Scaler
The
NOPScaler
assigns a scale equals to 1 to each input item, i.e., no scaling is applied upon calling theNOPScaler
.- 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]#
- Parameters
F – A module that can either refer to the Symbol API or the NDArray API in MXNet.
data – tensor containing the data to be scaled.
observed_indicator – observed_indicator: binary tensor with the same shape as
data
, that has 1 in correspondence of observed data points, and 0 in correspondence of missing data points.
- Returns
shape (N, C), identically equal to 1.
- Return type
Tensor
- class gluonts.mx.block.scaler.Scaler(keepdims: bool = False, axis: int = 1)[source]#
Bases:
mxnet.gluon.block.HybridBlock
Base class for blocks used to scale data.
- Parameters
keepdims – toggle to keep the dimension of the input tensor.
axis – specify the axis over which to scale. Default is 1 for (N, T, C) shaped input tensor.
- compute_scale(F, data: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], observed_indicator: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol])[source]#
Computes the scale of the given input data.
- Parameters
F – A module that can either refer to the Symbol API or the NDArray API in MXNet.
data – tensor containing the data to be scaled.
observed_indicator – observed_indicator: binary tensor with the same shape as
data
, that has 1 in correspondence of observed data points, and 0 in correspondence of missing data points.
- hybrid_forward(F, data: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], observed_indicator: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) Tuple[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]] [source]#
- Parameters
F – A module that can either refer to the Symbol API or the NDArray API in MXNet.
data – tensor containing the data to be scaled.
observed_indicator – observed_indicator: binary tensor with the same shape as
data
, that has 1 in correspondence of observed data points, and 0 in correspondence of missing data points.
- Returns
Tensor – Tensor containing the “scaled” data.
Tensor – Tensor containing the scale: this has the same shape as the data, except for the axis
axis
along which the scale is computed, which is removed ifkeepdims == False
, and kept with length 1 otherwise. For example, ifdata
has shape(N, T, C)
andaxis ==1 ``, then ``scale
has shape(N, C)
ifkeepdims == False
, and(N, 1, C)
otherwise.