def test_yj_transform_2lam():
lam = 2
# test for lambda == 2
res = _yj_transform_y(np.array(y), lam=lam) # test a copy...
assert res is not y
c1 = (lambda x: (np.power(x + 1, lam) - 1.0) / lam)
c4 = (lambda x: -np.log(-x + 1))
expected = np.array([
# case 4
c4(-2.), c4(-1.),
# case 1
c1(0), c1(1), c1(2)
])
assert_array_almost_equal(expected, res)
def test_yj_transform_0lam():
lam = 0
# test for lambda == 0
res = _yj_transform_y(np.array(y), lam=lam) # test a copy...
assert res is not y
# denom = 2.0 - lam
# numer = np.power((-x + 1), (2.0 - lam)) - 1.0
# return -numer / denom
c3 = (lambda x: -(np.power((-x + 1), (2.0 - lam)) - 1.0) / (2. - lam))
expected = np.array([
# case 3
c3(-2.), c3(-1.),
# case 2
np.log(0 + 1.), np.log(1 + 1.), np.log(2 + 1.)
])
assert_array_almost_equal(expected, res)