Source code for gluonts.mx.representation.representation_chain

# 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 List, Optional, Tuple

import mxnet as mx

# Standard library imports
import numpy as np

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

from .representation import Representation


[docs]class RepresentationChain(Representation): """ A class representing a hybrid approach of combining multiple representations into a single representation. Representations will be combined by concatenating them on dim=-1. Parameters ---------- chain A list of representations. Elements must be of type Representation. """ @validated() def __init__(self, chain: List, *args, **kwargs): super().__init__(*args, **kwargs) self.chain = chain for representation in self.chain: self.register_child(representation)
[docs] def initialize_from_dataset( self, input_dataset: Dataset, ctx: mx.Context = get_mxnet_context() ): for representation in self.chain: representation.initialize_from_dataset(input_dataset, ctx)
[docs] def initialize_from_array( self, input_array: np.ndarray, ctx: mx.Context = get_mxnet_context() ): for representation in self.chain: representation.initialize_from_array(input_array, ctx)
# 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]]: for representation in self.chain: data, scale, rep_params = representation( data, observed_indicator, scale, rep_params, ) return data, scale, rep_params
[docs] def post_transform( self, F, samples: Tensor, scale: Tensor, rep_params: List[Tensor] ) -> Tensor: for representation in self.chain[::-1]: samples = representation.post_transform( F, samples, scale, rep_params, ) return samples