def test_TangentSpace_inversetransform():
"""Test inverse transform of Tangent Space"""
covset = generate_cov(10,3)
ts = TangentSpace(metric='riemann')
ts.fit(covset)
t = ts.transform(covset)
cov = ts.inverse_transform(t)
assert_array_almost_equal(covset,cov)
def test_TangentSpace_inversetransform():
"""Test inverse transform of Tangent Space."""
covset = generate_cov(10, 3)
ts = TangentSpace(metric='riemann')
ts.fit(covset)
t = ts.transform(covset)
cov = ts.inverse_transform(t)
assert_array_almost_equal(covset, cov)
def test_TangentSpace_init(fit, tsupdate, metric, get_covmats):
n_trials, n_channels = 4, 3
n_ts = (n_channels * (n_channels + 1)) // 2
covmats = get_covmats(n_trials, n_channels)
ts = TangentSpace(metric=metric, tsupdate=tsupdate)
if fit:
ts.fit(covmats)
Xtr = ts.transform(covmats)
assert Xtr.shape == (n_trials, n_ts)
def fit_representation(self):
print(np.array(self.data).shape)
for k in range(len(self.data)):
subject_data = np.array(self.data[k])
print(subject_data.shape)
subject_labels = self.labels[k]
model_xDawn_enCours = pyriemann.estimation.XdawnCovariances(
4, xdawn_estimator='lwf')
subject_data = model_xDawn_enCours.fit_transform(
subject_data, subject_labels)
self.model_xDawn.append(model_xDawn_enCours)
model_tangentSpace_enCours = TangentSpace(metric='riemann')
model_tangentSpace_enCours.fit(subject_data, subject_labels)
self.model_tangentSpace.append(model_tangentSpace_enCours)
def test_TangentSpace_transform():
"""Test transform of Tangent Space."""
covset = generate_cov(10, 3)
ts = TangentSpace(metric='riemann')
ts.fit(covset)
ts.transform(covset)
X = np.zeros(shape=(10, 9))
assert_raises(ValueError, ts.transform, X)
X = np.zeros(shape=(10, 9, 8))
assert_raises(ValueError, ts.transform, X)
X = np.zeros(shape=(10))
assert_raises(ValueError, ts.transform, X)
X = np.zeros(shape=(12, 8, 8))
assert_raises(ValueError, ts.transform, X)
def test_TangentSpace_transform_with_ts_update():
"""Test transform of Tangent Space with TSupdate"""
covset = generate_cov(10,3)
ts = TangentSpace(metric='riemann',tsupdate=True)
ts.fit(covset)
ts.transform(covset)
def test_TangentSpace_transform():
"""Test transform of Tangent Space"""
covset = generate_cov(10,3)
ts = TangentSpace(metric='riemann')
ts.fit(covset)
ts.transform(covset)
def test_TangentSpace_fit():
"""Test Fit of Tangent Space"""
covset = generate_cov(10,3)
ts = TangentSpace(metric='riemann')
ts.fit(covset)
def test_TangentSpace_transform_with_ts_update():
"""Test transform of Tangent Space with TSupdate."""
covset = generate_cov(10, 3)
ts = TangentSpace(metric='riemann', tsupdate=True)
ts.fit(covset)
ts.transform(covset)
def test_TangentSpace_transform():
"""Test transform of Tangent Space."""
covset = generate_cov(10, 3)
ts = TangentSpace(metric='riemann')
ts.fit(covset)
ts.transform(covset)
def test_TangentSpace_fit():
"""Test Fit of Tangent Space."""
covset = generate_cov(10, 3)
ts = TangentSpace(metric='riemann')
ts.fit(covset)
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,
160, 170, 180
]
