Table Of Contents
Table Of Contents

Source code for gluonts.kernels._kernel_output

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#
# 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
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# 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
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# Standard library imports
from typing import Dict, Tuple

import numpy as np
from mxnet import gluon

# First-party imports
from gluonts.core.component import DType, validated
from gluonts.distribution.distribution_output import ArgProj
from gluonts.model.common import Tensor

# Relative imports
from . import Kernel


[docs]class KernelOutput: """ Class to connect a network to a kernel. """
[docs] def get_args_proj(self, float_type: DType) -> 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: DType = np.float32) -> ArgProj: """ 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 ------- ArgProj """ return ArgProj( args_dim=self.args_dim, domain_map=gluon.nn.HybridLambda(self.domain_map), float_type=float_type, )
# noinspection PyMethodOverriding,PyPep8Naming
[docs] def gp_params_scaling( self, F, past_target: Tensor, past_time_feat: Tensor ) -> Tuple[Tensor, Tensor, Tensor]: 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 ------- Kernel Instantiated specified Kernel subclass object. """ return self.kernel_cls(*kernel_args)