def test_JFATrainInitialize():
    # Check that the initialization is consistent and using the rng (cf. issue #118)

    eps = 1e-10

    # UBM GMM
    ubm = GMMMachine(2, 3)
    ubm.mean_supervector = UBM_MEAN
    ubm.variance_supervector = UBM_VAR

    ## JFA
    jfa_base = JFABase(ubm, 512, 4)
    # first round
    jfa_machine = JFAMachine(jfa_base)
    jfa_trainer = JFATrainer(jfa_machine)
    training_data = scipy.io.loadmat("./data/stats/fa_train_eigenvoices_stats.mat")
    jfa_trainer.train(training_data)