def test_robust_skewness_3d(self, reset_randomstate):
x = np.random.standard_normal(100)
x = np.hstack([x, np.zeros(1), -x])
x = np.tile(x, (10, 10, 1))
sk_3d = robust_skewness(x, axis=2)
result = np.zeros((10, 10))
for sk in sk_3d:
assert_almost_equal(sk, result)
def test_robust_skewness_4(self, reset_randomstate):
x = np.random.standard_normal(1000)
x[x > 0] *= 3
m = np.median(x)
s = x.std(ddof=0)
expected = (x.mean() - m) / s
_, _, _, sk4 = robust_skewness(x)
assert_allclose(expected, sk4)
def test_robust_skewness_3d(self):
x = np.random.standard_normal(100)
x = np.hstack([x, np.zeros(1), -x])
x = np.tile(x, (10, 10, 1))
sk_3d = robust_skewness(x, axis=2)
result = np.zeros((10, 10))
for sk in sk_3d:
assert_almost_equal(sk, result)
def test_robust_skewness_symmetric(self, reset_randomstate):
x = np.random.standard_normal(100)
x = np.hstack([x, np.zeros(1), -x])
sk = robust_skewness(x)
assert_almost_equal(np.array(sk), np.zeros(4))
def test_robust_skewness_1d_2d(self, reset_randomstate):
x = np.random.randn(21)
y = x[:, None]
sk_x = robust_skewness(x)
sk_y = robust_skewness(y, axis=None)
assert_almost_equal(np.array(sk_x), np.array(sk_y))
def test_robust_skewness_1d(self):
x = np.arange(21.0)
sk = robust_skewness(x)
assert_almost_equal(np.array(sk), np.zeros(4))
def test_robust_skewness_symmetric(self):
x = np.random.standard_normal(100)
x = np.hstack([x, np.zeros(1), -x])
sk = robust_skewness(x)
assert_almost_equal(np.array(sk), np.zeros(4))
def test_robust_skewness_1d_2d(self):
x = np.random.randn(21)
y = x[:, None]
sk_x = robust_skewness(x)
sk_y = robust_skewness(y, axis=None)
assert_almost_equal(np.array(sk_x), np.array(sk_y))
def test_robust_skewness_1d(self):
x = np.arange(21.0)
sk = robust_skewness(x)
assert_almost_equal(np.array(sk), np.zeros(4))