def test_fat_rectangular_matrices_with_square_padding_with_lapack_driver(nrow):
r"""Test Symmetric Procrustes with random wide matrices and non-default lapack driver."""
# generate random rectangular matrices
ncol = np.random.randint(nrow + 1, nrow + 10)
array_a, array_b = np.random.random((nrow, ncol)), np.random.random(
(nrow, ncol))
# minimize objective function to find transformation matrix
_, desired_func = minimize_one_transformation(array_a, array_b, ncol)
res = symmetric(array_a, array_b, pad=True, lapack_driver="gesdd")
# check results (solution is not uniqueness)
assert_almost_equal(np.abs(res.error - desired_func), 0.0, decimal=5)
assert_equal(res.s, None)
def test_random_tall_rectangular_matrices(ncol):
r"""Test Symmetric Procrustes with random tall matrices."""
# generate random floats in [0.0, 1.0) interval
nrow = np.random.randint(ncol, ncol + 10)
array_a, array_b = np.random.random((nrow, ncol)), np.random.random(
(nrow, ncol))
# minimize objective function to find transformation matrix
desired, desired_func = minimize_one_transformation(array_a, array_b, ncol)
# compute transformation & check results
res = symmetric(array_a, array_b, unpad_col=True, unpad_row=True)
assert_equal(res.s, None)
assert_almost_equal(np.abs(res.error - desired_func), 0.0, decimal=5)
assert_almost_equal(np.abs(res.t - desired), 0.0, decimal=3)