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)