def test_amari_distance(n=16, tol=1e-6):
"""Amari distance between matrix and its inverse should be 0."""
rng = np.random.RandomState(123)
A = rng.randn(n, n)
W = np.linalg.inv(A)
X = np.linalg.pinv(A)
np.testing.assert_allclose(amari(W, A), 0.0, rtol=tol)
np.testing.assert_allclose(amari(A, A), 2.912362, rtol=tol)
np.testing.assert_allclose(amari(X, A), 0.0, rtol=tol)
def test_orthomax(whiten_method):
rng = np.random.RandomState(123)
S = rng.laplace(size=(3, 500))
A = rng.random((3, 3))
s = Signal1D(A @ S)
s.decomposition()
s.blind_source_separation(3, algorithm="orthomax", whiten_method=whiten_method)
W = s.learning_results.unmixing_matrix
assert amari(W, A) < 0.5
# Verify that we can change gamma for orthomax method
s = artificial_data.get_core_loss_eels_line_scan_signal()
s.decomposition()
s.blind_source_separation(2, algorithm="orthomax", gamma=2)