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@@ -48,8 +48,11 @@ def generate_data_model(_filename, _transformations, _dataset_folder, _selected_
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if not os.path.exists(os.path.join(output_data_folder, _filename)):
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os.makedirs(os.path.join(output_data_folder, _filename))
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- train_file_data = []
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- test_file_data = []
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+ train_file = open(output_train_filename, 'w')
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+ test_file = open(output_test_filename, 'w')
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+
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+ # train_file_data = []
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+ # test_file_data = []
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# specific number of zones (zones indices)
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zones = np.arange(16)
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@@ -178,24 +181,23 @@ def generate_data_model(_filename, _transformations, _dataset_folder, _selected_
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line = line + '\n'
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if id_zone in train_zones:
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- train_file_data.append(line)
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+ # train_file_data.append(line)
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+ train_file.write(line)
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else:
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- test_file_data.append(line)
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+ # test_file_data.append(line)
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+ test_file.write(line)
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# remove first element (sliding window)
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del sequence_data[0]
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- train_file = open(output_train_filename, 'w')
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- test_file = open(output_test_filename, 'w')
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-
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- random.shuffle(train_file_data)
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- random.shuffle(test_file_data)
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+ # random.shuffle(train_file_data)
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+ # random.shuffle(test_file_data)
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- for line in train_file_data:
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- train_file.write(line)
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+ # for line in train_file_data:
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+ # train_file.write(line)
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- for line in test_file_data:
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- test_file.write(line)
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+ # for line in test_file_data:
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+ # test_file.write(line)
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train_file.close()
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test_file.close()
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