def transform(self, X):
"""Apply transform to covariances
Parameters
----------
covs: list of array
list of covariance matrices, shape (n_rois, n_rois)
Returns
-------
list of array, transformed covariance matrices,
shape (n_rois * (n_rois+1)/2,)
"""
covs = [self.cov_estimator_.fit(x).covariance_ for x in X]
covs = spd_mfd.my_stack(covs)
if self.kind == 'tangent':
covs = [spd_mfd.logm(self.whitening_.dot(c).dot(self.whitening_))
for c in covs]
elif self.kind == 'precision':
covs = [spd_mfd.inv(g) for g in covs]
elif self.kind == 'partial correlation':
covs = [prec_to_partial(spd_mfd.inv(g)) for g in covs]
elif self.kind == 'correlation':
covs = [cov_to_corr(g) for g in covs]
else:
raise ValueError("Unknown connectivity measure.")
return np.array([sym_to_vec(c) for c in covs])
def test_inv():
"""Testing inv function"""
m = my_mfd.random_spd(41)
m_inv = my_mfd.inv(m)
assert_array_almost_equal(m.dot(m_inv), np.eye(41))
