rnn-tensorflow.py

def read_signals(filename):
    with open(filename, 'r') as fp:
        data = fp.read().splitlines()
        data = map(lambda x: x.rstrip().lstrip().split(), data)
        data = [list(map(float, line)) for line in data]
        data = np.array(data, dtype=np.float32)
    return data
 
def read_labels(filename):        
    with open(filename, 'r') as fp:
        activities = fp.read().splitlines()
        activities = list(map(int, activities))
    return np.array(activities)
 
def randomize(dataset, labels):
    permutation = np.random.permutation(labels.shape[0])
    shuffled_dataset = dataset[permutation, :, :]
    shuffled_labels = labels[permutation]
    return shuffled_dataset, shuffled_labels
 
def one_hot_encode(np_array, num_labels):
    return (np.arange(num_labels) == np_array[:,None]).astype(np.float32)
 
def reformat_data(dataset, labels):
    no_labels = len(np.unique(labels))
    labels = one_hot_encode(labels, no_labels)
    dataset, labels = randomize(dataset, labels)
    return dataset, labels
 
####
 
INPUT_FOLDER_TRAIN = './UCI_HAR/train/InertialSignals/'
INPUT_FOLDER_TEST = './UCI_HAR/test/InertialSignals/'
 
INPUT_FILES_TRAIN = ['body_acc_x_train.txt', 'body_acc_y_train.txt', 'body_acc_z_train.txt', 
                     'body_gyro_x_train.txt', 'body_gyro_y_train.txt', 'body_gyro_z_train.txt',
                     'total_acc_x_train.txt', 'total_acc_y_train.txt', 'total_acc_z_train.txt']
 
INPUT_FILES_TEST = ['body_acc_x_test.txt', 'body_acc_y_test.txt', 'body_acc_z_test.txt', 
                     'body_gyro_x_test.txt', 'body_gyro_y_test.txt', 'body_gyro_z_test.txt',
                     'total_acc_x_test.txt', 'total_acc_y_test.txt', 'total_acc_z_test.txt']
 
#####
 
train_signals, test_signals = [], []
for input_file in INPUT_FILES_TRAIN:
    signal = read_signals(INPUT_FOLDER_TRAIN + input_file)
    train_signals.append(signal)
train_signals = np.transpose(np.array(train_signals), (1, 2, 0))
 
for input_file in INPUT_FILES_TEST:
    signal = read_signals(INPUT_FOLDER_TEST + input_file)
    test_signals.append(signal)
test_signals = np.transpose(np.array(test_signals), (1, 2, 0))
 
#####
 
LABELFILE_TRAIN = './UCI_HAR/train/y_train.txt'
LABELFILE_TEST = './UCI_HAR/test/y_test.txt'
train_labels = read_labels(LABELFILE_TRAIN)
test_labels = read_labels(LABELFILE_TEST)
 
#####
 
train_dataset, train_labels = reformat_data(train_signals, train_labels)
test_dataset, test_labels = reformat_data(test_signals, test_labels)