| # Lint as: python3 |
| # Copyright 2019 The TensorFlow Authors. 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. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License 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. |
| # ============================================================================== |
| # pylint: disable=g-bad-import-order |
| |
| """Data augmentation that will be used in data_load.py.""" |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import random |
| |
| import numpy as np |
| |
| |
| def time_wrapping(molecule, denominator, data): |
| """Generate (molecule/denominator)x speed data.""" |
| tmp_data = [[0 |
| for i in range(len(data[0]))] |
| for j in range((int(len(data) / molecule) - 1) * denominator)] |
| for i in range(int(len(data) / molecule) - 1): |
| for j in range(len(data[i])): |
| for k in range(denominator): |
| tmp_data[denominator * i + |
| k][j] = (data[molecule * i + k][j] * (denominator - k) + |
| data[molecule * i + k + 1][j] * k) / denominator |
| return tmp_data |
| |
| |
| def augment_data(original_data, original_label): |
| """Perform data augmentation.""" |
| new_data = [] |
| new_label = [] |
| for idx, (data, label) in enumerate(zip(original_data, original_label)): # pylint: disable=unused-variable |
| # Original data |
| new_data.append(data) |
| new_label.append(label) |
| # Sequence shift |
| for num in range(5): # pylint: disable=unused-variable |
| new_data.append((np.array(data, dtype=np.float32) + |
| (random.random() - 0.5) * 200).tolist()) |
| new_label.append(label) |
| # Random noise |
| tmp_data = [[0 for i in range(len(data[0]))] for j in range(len(data))] |
| for num in range(5): |
| for i in range(len(tmp_data)): # pylint: disable=consider-using-enumerate |
| for j in range(len(tmp_data[i])): |
| tmp_data[i][j] = data[i][j] + 5 * random.random() |
| new_data.append(tmp_data) |
| new_label.append(label) |
| # Time warping |
| fractions = [(3, 2), (5, 3), (2, 3), (3, 4), (9, 5), (6, 5), (4, 5)] |
| for molecule, denominator in fractions: |
| new_data.append(time_wrapping(molecule, denominator, data)) |
| new_label.append(label) |
| # Movement amplification |
| for molecule, denominator in fractions: |
| new_data.append( |
| (np.array(data, dtype=np.float32) * molecule / denominator).tolist()) |
| new_label.append(label) |
| return new_data, new_label |