def test_standard_scale_with_bessel_correction_versus_sklearn(A):
data = A.value
df = pd.DataFrame(data)
def zscore(data):
return (data - data.mean()) / data.std(ddof=1)
expected = df.apply(zscore).values
output = standard(data, ddof=1)
assert np.allclose(expected, output)
def test_standard_scale_raises_if_ddof_ne_0_or_1():
data = np.arange(20, dtype=float).reshape(2, 10)
for ddof in -1, 2:
with raises(ValueError):
_ = standard(data, ddof=ddof)
def test_standard_scale_versus_sklearn(A):
data = A.value
expected = StandardScaler().fit_transform(data)
output = standard(data)
assert np.allclose(expected, output)
def test_sklearn_dataset(dataset):
data = standard(dataset.value)
A = data.T @ data
check_versus_numpy(A, qr_jit)
