def test_covariances():
"""Test fit Covariances"""
x = np.random.randn(2, 3, 100)
cov = Covariances()
cov.fit(x)
cov.fit_transform(x)
assert_equal(cov.get_params(), dict(estimator='scm'))
def test_covariances():
"""Test fit Covariances"""
x = np.random.randn(2, 3, 100)
cov = Covariances()
cov.fit(x)
cov.fit_transform(x)
assert_equal(cov.get_params(), dict(estimator='scm'))
def test_covariances(estimator, rndstate):
"""Test Covariances"""
n_matrices, n_channels, n_times = 2, 3, 100
x = rndstate.randn(n_matrices, n_channels, n_times)
cov = Covariances(estimator=estimator)
cov.fit(x)
covmats = cov.fit_transform(x)
assert cov.get_params() == dict(estimator=estimator)
assert covmats.shape == (n_matrices, n_channels, n_channels)
assert is_spd(covmats)
f_name = dire + '/' + name
subject = name.split('_')
data = loadmat(f_name)
data_IS = data[list(data.keys())[-1]]
data_tensor = [data_IS[0][0]]
for j in range(len(data_IS)):
if j == 0:
k = 1
else:
k = 0
for i in range(k, len(data_IS[j])):
temp = [data_IS[j][i]]
data_tensor = np.concatenate((data_tensor, temp), axis=0)
cov = Covariances(estimator='lwf')
ts = TangentSpace()
cov.fit(data_tensor, label)
cov_train = cov.transform(data_tensor)
ts.fit(cov_train, label)
ts_train = ts.transform(cov_train)
ts_shape = (np.shape(ts_train))
pca = PCA()
ann = MLPClassifier(max_iter=5000)
clf = BaggingClassifier(base_estimator=ann, bootstrap=True)
pipe = Pipeline(steps=[('pca', pca), ('clf', clf)])
param_grid = {
'pca__n_components': [20, 30, 40, 50, 60, 70, 80, 90, 100],
'clf__base_estimator__hidden_layer_sizes':
[(10), (20), (30), (40), (50), (60), (70), (80), (90), (100), (110),
(120), (130), (140), (150), (160), (170), (180)],
'clf__n_estimators': [
10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150,
def test_covariances():
"""Test fit Covariances"""
x = np.random.randn(2, 3, 100)
cov = Covariances()
cov.fit(x)
cov.fit_transform(x)
def test_covariances():
"""Test fit Covariances"""
x = np.random.randn(2,3,100)
cov = Covariances()
cov.fit(x)
cov.fit_transform(x)