def load_semi_supervised():

    train_set, test_set, valid_set = _download()

    # Add 6 static labels.
    train_x_l = np.zeros((6, 2))
    train_t_l = np.array([0, 0, 0, 1, 1, 1])
    # Top halfmoon
    train_x_l[0] = [.7, 1.7]  # left
    train_x_l[1] = [1.6, 2.6]  # middle
    train_x_l[2] = [2.7, 1.7]  # right

    # Bottom halfmoon
    train_x_l[3] = [1.6, 2.0]  # left
    train_x_l[4] = [2.7, 1.1]  # middle
    train_x_l[5] = [3.5, 2.0]  # right
    train_set_labeled = (train_x_l, train_t_l)

    train_set_labeled = pad_targets(train_set_labeled)
    train_set = pad_targets(train_set)
    test_set = pad_targets(test_set)
    if valid_set is not None:
        valid_set = pad_targets(valid_set)

    return train_set, train_set_labeled, test_set, valid_set
Read the command line and either start or stop the daemon
dataset/ETHPy150Open larsmaaloee/auxiliary-deep-generative-models/data_loaders/half_moon.py/load_semi_supervised--crossover with--dataset/ETHPy150Open kiberpipa/Intranet/pipa/ltsp/src/ltsp_usage_monitor/daemon.py/Daemon.main