def test_TangentSpace_inversetransform_without_fit():
"""Test inverse transform of Tangent Space without fit."""
covset = generate_cov(10, 3)
ts = TangentSpace(metric='identity')
tsv = ts.fit_transform(covset)
ts = TangentSpace(metric='riemann')
cov = ts.inverse_transform(tsv)
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)
p, F = p_test.test(epochs_data, labels)
duration = time() - t_init
fig, axes = plt.subplots(1, 1, figsize=[6, 3], sharey=True)
p_test.plot(nbins=10, axes=axes)
plt.title('Pairwise distance - %.2f sec.' % duration)
print('p-value: %.3f' % p)
sns.despine()
plt.tight_layout()
plt.show()
###############################################################################
# Classification based permutation test
###############################################################################
clf = make_pipeline(XdawnCovariances(2), TangentSpace('logeuclid'),
LogisticRegression())
t_init = time()
p_test = PermutationModel(n_perms, model=clf, cv=3)
p, F = p_test.test(epochs_data, labels)
duration = time() - t_init
fig, axes = plt.subplots(1, 1, figsize=[6, 3], sharey=True)
p_test.plot(nbins=10, axes=axes)
plt.title('Classification - %.2f sec.' % duration)
print('p-value: %.3f' % p)
sns.despine()
plt.tight_layout()
plt.show()
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_without_fit():
"""Test transform of Tangent Space without fit."""
covset = generate_cov(10, 3)
ts = TangentSpace(metric='riemann')
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_init():
"""Test init of Tangent."""
TangentSpace(metric='riemann')
verbose=False)
labels = epochs.events[:, -1]
evoked = epochs.average()
###############################################################################
# Decoding in tangent space with a logistic regression
n_components = 2 # pick some components
# Define a monte-carlo cross-validation generator (reduce variance):
cv = KFold(n_splits=10, shuffle=True, random_state=42)
epochs_data = epochs.get_data()
clf = make_pipeline(XdawnCovariances(n_components),
TangentSpace(metric='riemann'), LogisticRegression())
preds = np.zeros(len(labels))
for train_idx, test_idx in cv.split(epochs_data):
y_train, y_test = labels[train_idx], labels[test_idx]
clf.fit(epochs_data[train_idx], y_train)
preds[test_idx] = clf.predict(epochs_data[test_idx])
# Printing the results
acc = np.mean(preds == labels)
print("Classification accuracy: %f " % (acc))
names = ['audio left', 'audio right', 'vis left', 'vis right']
plot_confusion_matrix(preds, labels, names)