Table Of Contents
Table Of Contents

gluonts.kernels package

class gluonts.kernels.Kernel[source]

Bases: object

kernel_matrix(x1: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], x2: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol])[source]
class gluonts.kernels.PeriodicKernel(amplitude: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], length_scale: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], frequency: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], F=None)[source]

Bases: gluonts.kernels._kernel.Kernel

Computes a covariance matrix based on the Periodic kernel between inputs \(\mathbf{x_1}\) and \(\mathbf{x_2}\): \(k_{\text{Per}}(\mathbf{x_1}, \mathbf{x_2}) = \theta_0 \exp \left (\frac{-2\sin^2(\theta_2 \pi \|\mathbf{x_1} - \mathbf{x_2}\|)} {\theta_1^2} \right)\), where \(\theta_0\) is the amplitude parameter, \(\theta_1\) is the length scale parameter and \(\theta_2\) is the frequency parameter.

kernel_matrix(x1: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], x2: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]
Parameters:
  • x1 (Tensor) – Feature data of shape (batch_size, history_length, num_features).
  • x2 (Tensor) – Feature data of shape (batch_size, history_length, num_features).
Returns:

Periodic kernel matrix of shape (batch_size, history_length, history_length).

Return type:

Tensor

class gluonts.kernels.RBFKernel(amplitude: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], length_scale: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], F=None)[source]

Bases: gluonts.kernels._kernel.Kernel

Computes a covariance matrix based on the RBF (squared exponential) kernel between inputs \(\mathbf{x_1}\) and \(\mathbf{x_2}\): \(k_{\text{RBF}}(\mathbf{x_1}, \mathbf{x_2}) = \theta_0 \exp \left ( -\frac{\|\mathbf{x_1} - \mathbf{x_2}\|^2} {2\theta_1^2} \right)\), where \(\theta_0\) is the amplitude parameter and \(\theta_1\) is the length scale parameter.

kernel_matrix(x1: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], x2: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]
Parameters:
  • x1 (Tensor) – Feature data of shape (batch_size, history_length, num_features).
  • x2 (Tensor) – Feature data of shape (batch_size, history_length, num_features).
Returns:

RBF kernel matrix of shape (batch_size, history_length, history_length).

Return type:

Tensor

class gluonts.kernels.PeriodicKernelOutput[source]

Bases: gluonts.kernels._kernel_output.KernelOutputDict

args_dim = {'amplitude': 1, 'frequency': 1, 'length_scale': 1}
classmethod domain_map(F, amplitude: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], length_scale: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], frequency: 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], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]][source]

This function applies the softmax to the Periodic Kernel hyper-parameters.

Parameters:
  • F (ModuleType) – A module that can either refer to the Symbol API or the NDArray API in MXNet.
  • amplitude (Tensor) – Periodic kernel amplitude hyper-parameter of shape (batch_size, 1, 1).
  • length_scale (Tensor) – Periodic kernel length scale hyper-parameter of of shape (batch_size, 1, 1).
  • frequency (Tensor) – Periodic kernel hyper-parameter of shape (batch_size, 1, 1).
Returns:

Three GP Periodic kernel hyper-parameters. Each is a Tensor of shape: (batch_size, 1, 1).

Return type:

Tuple

gp_params_scaling(F, past_target: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], past_time_feat: 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], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]][source]

This function returns the scales for the GP Periodic Kernel hyper-parameters by using the standard deviations of the past_target and past_time_features.

Parameters:
  • F (ModuleType) – A module that can either refer to the Symbol API or the NDArray API in MXNet.
  • past_target (Tensor) – Training time series values of shape (batch_size, context_length).
  • past_time_feat (Tensor) – Training features of shape (batch_size, context_length, num_features).
Returns:

Three scaled GP hyper-parameters for the Periodic Kernel and scaled model noise hyper-parameter. Each is a Tensor of shape (batch_size, 1, 1).

Return type:

Tuple

kernel_cls

alias of PeriodicKernel

class gluonts.kernels.RBFKernelOutput[source]

Bases: gluonts.kernels._kernel_output.KernelOutputDict

args_dim = {'amplitude': 1, 'length_scale': 1}
classmethod domain_map(F, amplitude: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], length_scale: 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]

This function applies the softmax to the RBF Kernel hyper-parameters.

Parameters:
  • F (mx.symbol or mx.nd) – A module that can either refer to the Symbol API or the NDArray API in MXNet.
  • amplitude – RBF kernel amplitude hyper-parameter of shape (batch_size, 1, 1).
  • length_scale – RBF kernel length scale hyper-parameter of of shape (batch_size, 1, 1).
Returns:

Two GP RBF kernel hyper-parameters. Each is a Tensor of shape: (batch_size, 1, 1).

Return type:

Tuple

gp_params_scaling(F, past_target: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], past_time_feat: 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], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]][source]

This function returns the scales for the GP RBF Kernel hyper-parameters by using the standard deviations of the past_target and past_time_features.

Parameters:
  • F – A module that can either refer to the Symbol API or the NDArray API in MXNet.
  • past_target – Training time series values of shape (batch_size, context_length).
  • past_time_feat – Training features of shape (batch_size, context_length, num_features).
Returns:

Two scaled GP hyper-parameters for the RBF Kernel and scaled model noise hyper-parameter. Each is a Tensor of shape (batch_size, 1, 1).

Return type:

Tuple

kernel_cls

alias of RBFKernel

class gluonts.kernels.KernelOutput[source]

Bases: object

Class to connect a network to a kernel.

static compute_std(F, data: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], axis: int) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]

This function computes the standard deviation of the data along a given axis.

Parameters:
  • F (ModuleType) – A module that can either refer to the Symbol API or the NDArray API in MXNet.
  • data (Tensor) – Data to be used to compute the standard deviation.
  • axis (int) – Axis along which to compute the standard deviation.
Returns:

The standard deviation of the given data.

Return type:

Tensor

get_args_proj(float_type: gluonts.core.component.DType) → mxnet.gluon.block.HybridBlock[source]
kernel(args) → gluonts.kernels._kernel.Kernel[source]
class gluonts.kernels.KernelOutputDict[source]

Bases: gluonts.kernels._kernel_output.KernelOutput

domain_map(F, *args)[source]
get_args_proj(float_type: gluonts.core.component.DType = <class 'numpy.float32'>) → gluonts.distribution.distribution_output.ArgProj[source]

This method calls the ArgProj block in distribution_output to project from a dense layer to kernel arguments.

Parameters:float_type (DType) – Determines whether to use single or double precision.
Returns:
Return type:ArgProj
get_num_args() → int[source]
gp_params_scaling(F, past_target: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], past_time_feat: 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], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]][source]
kernel(kernel_args) → gluonts.kernels._kernel.Kernel[source]
Parameters:kernel_args – Variable length argument list.
Returns:Instantiated specified Kernel subclass object.
Return type:Kernel