def _compute_gamma_auto(self, X_train):
# mu = mean_riemann(X_train)
mu = mean_logeuclid(X_train)
sigma = 0
for matrix in X_train:
sigma += np.power(distance_logeuclid(matrix, mu), self.p)
self.gamma = len(X_train) / sigma
def test_distance_wrapper_random(met, gfunc, get_covmats):
n_trials, n_channels = 2, 5
covmats = get_covmats(n_trials, n_channels)
A, B = covmats[0], covmats[1]
if gfunc is geodesic_euclid:
Ctrue = mean_euclid(covmats)
elif gfunc is geodesic_logeuclid:
Ctrue = mean_logeuclid(covmats)
elif gfunc is geodesic_riemann:
Ctrue = mean_riemann(covmats)
assert geodesic(A, B, 0.5, metric=met) == approx(Ctrue)
def cal_riem_means(X1, X2, tORs, rType):
# find the mean of the covariances in X
# two options to use: rType == riem or rType == log-eucl
X = np.concatenate((X1, X2), axis=0)
reg_param = cal_shrinkage(X, tORs)
W1 = cal_covariance(X1, tORs, reg_param)
W2 = cal_covariance(X2, tORs, reg_param)
# find the riemannian means of the covariances
if rType == 'riem':
C1_mean = mr.mean_riemann(W1)
C2_mean = mr.mean_riemann(W2)
elif rType == 'log-eucl':
C1_mean = mr.mean_logeuclid(W1)
C2_mean = mr.mean_logeuclid(W2)
return np.array([C1_mean, C2_mean]), reg_param
def test_random_mat(self, geodesic_func, get_covmats):
n_trials, n_channels = 2, 5
covmats = get_covmats(n_trials, n_channels)
A, B = covmats[0], covmats[1]
if geodesic_func is geodesic_euclid:
Ctrue = mean_euclid(covmats)
elif geodesic_func is geodesic_logeuclid:
Ctrue = mean_logeuclid(covmats)
elif geodesic_func is geodesic_riemann:
Ctrue = mean_riemann(covmats)
self.geodesic_0(geodesic_func, A, B)
self.geodesic_1(geodesic_func, A, B)
self.geodesic_middle(geodesic_func, A, B, Ctrue)
def test_logeuclid_mean():
"""Test the logeuclidean mean"""
covmats, diags, A = generate_cov(100, 3)
C = mean_logeuclid(covmats)
def test_mean_covariance_logeuclid():
"""Test mean_covariance for logeuclid metric"""
covmats, diags, A = generate_cov(100, 3)
C = mean_covariance(covmats, metric='logeuclid')
Ctrue = mean_logeuclid(covmats)
assert_array_equal(C, Ctrue)
def test_mean_covariance_logeuclid():
"""Test mean_covariance for logeuclid metric"""
covmats = generate_cov(100, 3)
C = mean_covariance(covmats, metric='logeuclid')
Ctrue = mean_logeuclid(covmats)
assert_array_equal(C, Ctrue)
def test_logeuclid_mean():
"""Test the logeuclidean mean"""
covmats = generate_cov(100, 3)
C = mean_logeuclid(covmats)
