def test_make_augmented_matrix():
np.random.seed(1)
n = 500
x = np.random.uniform(-1, 1, (n, 3))
s = np.dot(x.T, x)
y = np.array(list(range(n)))
w = np.random.uniform(0, 1, n)
nobs, n_columns = x.shape
# matrix_sqrt removes redundant rows,
# if alpha is zero, then no augmentation is needed
alpha = 0
aug_y, aug_x, aug_w = make_augmented_matrix(y, x, alpha * s, w)
expected_aug_x = x
assert_allclose(aug_x, expected_aug_x)
expected_aug_y = y
expected_aug_y[:nobs] = y
assert_allclose(aug_y, expected_aug_y)
expected_aug_w = w
assert_allclose(aug_w, expected_aug_w)
alpha = 1
aug_y, aug_x, aug_w = make_augmented_matrix(y, x, s, w)
rs = matrix_sqrt(alpha * s)
# alternative version to matrix_sqrt using cholesky is not available
# rs = sp.linalg.cholesky(alpha * s)
assert_allclose(np.dot(rs.T, rs), alpha * s)
expected_aug_x = np.vstack([x, rs])
assert_allclose(aug_x, expected_aug_x)
expected_aug_y = np.zeros(shape=(nobs + n_columns,))
expected_aug_y[:nobs] = y
assert_allclose(aug_y, expected_aug_y)
expected_aug_w = np.concatenate((w, [1] * n_columns), axis=0)
assert_allclose(aug_w, expected_aug_w)
def test_make_augmented_matrix():
np.random.seed(1)
n = 500
x = np.random.uniform(-1, 1, (n, 3))
s = np.dot(x.T, x)
y = np.array(list(range(n)))
w = np.random.uniform(0, 1, n)
nobs, n_columns = x.shape
# matrix_sqrt removes redundant rows,
# if alpha is zero, then no augmentation is needed
alpha = 0
aug_y, aug_x, aug_w = make_augmented_matrix(y, x, alpha * s, w)
expected_aug_x = x
assert_allclose(aug_x, expected_aug_x)
expected_aug_y = y
expected_aug_y[:nobs] = y
assert_allclose(aug_y, expected_aug_y)
expected_aug_w = w
assert_allclose(aug_w, expected_aug_w)
alpha = 1
aug_y, aug_x, aug_w = make_augmented_matrix(y, x, s, w)
rs = matrix_sqrt(alpha * s)
# alternative version to matrix_sqrt using cholesky is not available
# rs = sp.linalg.cholesky(alpha * s)
assert_allclose(np.dot(rs.T, rs), alpha * s)
expected_aug_x = np.vstack([x, rs])
assert_allclose(aug_x, expected_aug_x)
expected_aug_y = np.zeros(shape=(nobs + n_columns, ))
expected_aug_y[:nobs] = y
assert_allclose(aug_y, expected_aug_y)
expected_aug_w = np.concatenate((w, [1] * n_columns), axis=0)
assert_allclose(aug_w, expected_aug_w)