mirror of
https://github.com/wassname/pytorch-ts.git
synced 2026-07-10 08:36:21 +08:00
Dr. Kashif Rasul
50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License").
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# You may not use this file except in compliance with the License.
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# A copy of the License is located at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# or in the "license" file accompanying this file. This file is distributed
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# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
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# express or implied. See the License for the specific language governing
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# permissions and limitations under the License.
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# Standard library imports
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import itertools
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# Third-party imports
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import torch
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# First-party imports
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from pts.model.deepar import DeepARTrainingNetwork
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def test_lagged_subsequences():
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N = 8
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T = 96
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C = 2
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lags = [1, 2, 3, 24, 48]
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I = len(lags)
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sequence = torch.randn((N, T, C))
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S = 48
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# (batch_size, sub_seq_len, target_dim, num_lags)
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lagged_subsequences = DeepARTrainingNetwork.get_lagged_subsequences(
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sequence=sequence,
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sequence_length=sequence.shape[1],
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indices=lags,
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subsequences_length=S,
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)
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assert (N, S, C, I) == lagged_subsequences.shape
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# checks that lags value behave as described as in the get_lagged_subsequences contract
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for i, j, k in itertools.product(range(N), range(S), range(I)):
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assert (
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(lagged_subsequences[i, j, :, k] == sequence[i, -lags[k] - S + j, :])
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.numpy()
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.all()
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)
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