Source code for

# Copyright 2018, 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
# or in the "license" file accompanying this file. This file is distributed
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.

from typing import List, Optional, Tuple

import mxnet as mx
import numpy as np
from mxnet.gluon import nn

from gluonts.core.component import validated
from gluonts.dataset.common import Dataset
from import Tensor
from import get_mxnet_context

[docs]class Representation(nn.HybridBlock): """ An abstract class representing input/output representations. """ @validated() def __init__(self): super().__init__()
[docs] def initialize_from_dataset( self, input_dataset: Dataset, ctx: mx.Context = get_mxnet_context() ): r""" Initialize the representation based on an entire dataset. Parameters ---------- input_dataset GluonTS dataset. ctx MXNet context. """ pass
[docs] def initialize_from_array( self, input_array: np.ndarray, ctx: mx.Context = get_mxnet_context() ): r""" Initialize the representation based on a numpy array. Parameters ---------- input_array Numpy array. ctx MXNet context. """ pass
# noinspection PyMethodOverriding
[docs] def hybrid_forward( self, F, data: Tensor, observed_indicator: Tensor, scale: Optional[Tensor], rep_params: List[Tensor], **kwargs, ) -> Tuple[Tensor, Tensor, List[Tensor]]: r""" 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[Tensor, Tensor, List[Tensor]] Tuple consisting of the transformed data, the computed scale, and additional parameters to be passed to post_transform. """ return data, F.ones_like(data), []
[docs] def post_transform( self, F, samples: Tensor, scale: Tensor, rep_params: List[Tensor] ) -> Tensor: r""" 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 ------- Tensor Post-transformed samples. """ return samples