Source code for gluonts.mx.representation.representation

# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# or in the "license" file accompanying this file. This file is distributed
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# Standard library imports
from typing import List, Optional, Tuple

import mxnet as mx
import numpy as np

# Third-party imports
from mxnet.gluon import nn

# First-party imports
from gluonts.core.component import get_mxnet_context, validated
from gluonts.dataset.common import Dataset
from gluonts.model.common import Tensor


[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