# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
from typing import Dict, Type
import numpy as np
from mxnet import gluon
from gluonts.core.component import validated
from gluonts.mx import Tensor
from gluonts.mx.distribution.distribution_output import ArgProj
from . import Kernel
[docs]class KernelOutput:
"""
Class to connect a network to a kernel.
"""
[docs] def get_args_proj(self, float_type: Type) -> gluon.HybridBlock:
raise NotImplementedError()
[docs] def kernel(self, args) -> Kernel:
raise NotImplementedError()
# noinspection PyMethodOverriding,PyPep8Naming
[docs] @staticmethod
def compute_std(F, data: Tensor, axis: int) -> Tensor:
"""
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
-------
Tensor
The standard deviation of the given data.
"""
return F.sqrt(
F.mean(
F.broadcast_minus(
data, F.mean(data, axis=axis).expand_dims(axis=axis)
)
** 2,
axis=axis,
)
)
[docs]class KernelOutputDict(KernelOutput):
args_dim: Dict[str, int]
kernel_cls: type
@validated()
def __init__(self) -> None:
pass
[docs] def get_num_args(self) -> int:
return len(self.args_dim)
[docs] def get_args_proj(self, float_type: Type = np.float32) -> ArgProj:
"""
This method calls the ArgProj block in distribution_output to project
from a dense layer to kernel arguments.
Parameters
----------
float_type : Type
Determines whether to use single or double precision.
Returns
-------
ArgProj
"""
return ArgProj(
args_dim=self.args_dim,
domain_map=gluon.nn.HybridLambda(self.domain_map),
dtype=float_type,
)
# noinspection PyMethodOverriding,PyPep8Naming
[docs] def gp_params_scaling(
self, F, past_target: Tensor, past_time_feat: Tensor
) -> tuple:
raise NotImplementedError()
# noinspection PyMethodOverriding,PyPep8Naming
[docs] def domain_map(self, F, *args: Tensor):
raise NotImplementedError()
[docs] def kernel(self, kernel_args) -> Kernel:
"""
Parameters
----------
kernel_args
Variable length argument list.
Returns
-------
gluonts.mx.kernels.Kernel
Instantiated specified Kernel subclass object.
"""
return self.kernel_cls(*kernel_args)