def METHOD_NAME():
    n, sz, d = 15, 10, 3
    rng = numpy.random.RandomState(0)
    X = rng.randn(n, sz, d)
    dba_km = TimeSeriesKMeans(n_clusters=3,
                              n_init=2,
                              metric="dtw",
                              verbose=True,
                              max_iter_barycenter=10)
    _check_not_fitted(dba_km)
    dba_km.fit(X)
    _check_params_predict(dba_km, X, ['predict'])
    sdtw_km = TimeSeriesKMeans(n_clusters=3,
                               metric="softdtw",
                               metric_params={"gamma": .01},
                               verbose=True)
    _check_not_fitted(sdtw_km)
    sdtw_km.fit(X)
    _check_params_predict(sdtw_km, X, ['predict'])