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)
