75 lines
3.1 KiB
Python
75 lines
3.1 KiB
Python
# Lint as: python3
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# Copyright 2019 The TensorFlow Authors. 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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# You may obtain a copy of the License 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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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# pylint: disable=g-bad-import-order
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"""Data augmentation that will be used in data_load.py."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import random
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import numpy as np
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def time_wrapping(molecule, denominator, data):
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"""Generate (molecule/denominator)x speed data."""
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tmp_data = [[0
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for i in range(len(data[0]))]
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for j in range((int(len(data) / molecule) - 1) * denominator)]
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for i in range(int(len(data) / molecule) - 1):
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for j in range(len(data[i])):
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for k in range(denominator):
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tmp_data[denominator * i +
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k][j] = (data[molecule * i + k][j] * (denominator - k) +
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data[molecule * i + k + 1][j] * k) / denominator
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return tmp_data
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def augment_data(original_data, original_label):
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"""Perform data augmentation."""
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new_data = []
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new_label = []
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for idx, (data, label) in enumerate(zip(original_data, original_label)): # pylint: disable=unused-variable
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# Original data
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new_data.append(data)
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new_label.append(label)
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# Sequence shift
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for num in range(5): # pylint: disable=unused-variable
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new_data.append((np.array(data, dtype=np.float32) +
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(random.random() - 0.5) * 200).tolist())
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new_label.append(label)
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# Random noise
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tmp_data = [[0 for i in range(len(data[0]))] for j in range(len(data))]
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for num in range(5):
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for i in range(len(tmp_data)): # pylint: disable=consider-using-enumerate
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for j in range(len(tmp_data[i])):
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tmp_data[i][j] = data[i][j] + 5 * random.random()
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new_data.append(tmp_data)
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new_label.append(label)
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# Time warping
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fractions = [(3, 2), (5, 3), (2, 3), (3, 4), (9, 5), (6, 5), (4, 5)]
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for molecule, denominator in fractions:
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new_data.append(time_wrapping(molecule, denominator, data))
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new_label.append(label)
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# Movement amplification
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for molecule, denominator in fractions:
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new_data.append(
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(np.array(data, dtype=np.float32) * molecule / denominator).tolist())
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new_label.append(label)
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return new_data, new_label
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