def test_Xdawncovariances(): """Test fit ERPCovariances""" x = np.random.randn(10, 3, 100) labels = np.array([0, 1]).repeat(5) cov = XdawnCovariances() cov.fit_transform(x, labels) assert_equal(cov.get_params(), dict(nfilter=4, applyfilters=True, classes=None, estimator='scm', xdawn_estimator='scm'))
def test_Xdawncovariances(): """Test fit ERPCovariances""" x = np.random.randn(10, 3, 100) labels = np.array([0, 1]).repeat(5) cov = XdawnCovariances() cov.fit_transform(x, labels) assert_equal(cov.get_params(), dict(nfilter=4, applyfilters=True, classes=None, estimator='scm', xdawn_estimator='scm', baseline_cov=None))
def test_xdawn_covariances_nfilter(nfilter, rndstate, get_labels): """Test fit XdawnCovariances""" n_classes, n_matrices, n_channels, n_times = 2, 4, 8, 100 x = rndstate.randn(n_matrices, n_channels, n_times) labels = get_labels(n_matrices, n_classes) cov = XdawnCovariances(nfilter=nfilter) covmats = cov.fit_transform(x, labels) assert cov.get_params() == dict( nfilter=nfilter, applyfilters=True, classes=None, estimator="scm", xdawn_estimator="scm", baseline_cov=None, ) covsize = 2 * (n_classes * nfilter) assert covmats.shape == (n_matrices, covsize, covsize) assert is_spsd(covmats)