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 wrapper_distance_logeuclid(A, B, args):
"""
A wrapper for pyriemann.utils.distance.distance_logeuclid to handle I/O with
regards to reducing samples classes.
For function doc refer to the doc of pyriemann.utils.mean.distance_logeuclid.
"""
return distance_logeuclid(A, B)
def cal_riemann_distance(X, Cmean, rType):
# find the distance of the covariances in X with the average covariance Cmean
# two options to use: rType == riem or rType == log-eucl
num_trials = np.size(X, axis=0)
output = np.zeros((num_trials, 1))
if rType == 'riem':
for ik in range(num_trials):
output[ik] = dst.distance_riemann(X[ik], Cmean)
elif rType == 'log-eucl':
for ik in range(num_trials):
output[ik] = dst.distance_logeuclid(X[ik], Cmean)
return output
def test_distance_generic_custom():
"""Test custom distance for generic function"""
A = 2*np.eye(3)
B = 2*np.eye(3)
assert_equal(distance(A, B, metric=distance_logeuclid),
distance_logeuclid(A, B))
def test_distance_generic_logeuclid():
"""Test logeuclid distance for generic function"""
A = 2*np.eye(3)
B = 2*np.eye(3)
assert_equal(distance(A, B, metric='logeuclid'), distance_logeuclid(A, B))
def test_distance_logeuclid():
"""Test logeuclid distance"""
A = 2*np.eye(3)
B = 2*np.eye(3)
assert_equal(distance_logeuclid(A, B), 0)
def test_distance_generic_logeuclid():
"""Test logeuclid distance for generic function"""
A = 2 * np.eye(3)
B = 2 * np.eye(3)
assert_equal(distance(A, B, metric='logeuclid'), distance_logeuclid(A, B))
def test_distance_logeuclid():
"""Test logeuclid distance"""
A = 2 * np.eye(3)
B = 2 * np.eye(3)
assert_equal(distance_logeuclid(A, B), 0)
def test_distance_generic_custom():
"""Test custom distance for generic function"""
A = 2 * np.eye(3)
B = 2 * np.eye(3)
assert_equal(distance(A, B, metric=distance_logeuclid),
distance_logeuclid(A, B))
def test_distance_logeuclid():
"""Test logeuclid distance"""
A = 2 * np.eye(3)
B = 2 * np.eye(3)
assert distance_logeuclid(A, B) == 0
