def test_sinc_n_components():
rng = np.random.RandomState(42)
X = rng.uniform(-1, 1, [100, 1])
Y_true = np.sign(np.cos(4. * np.pi * X))
ka_gt = kanalysis(X, Y_true, n_components=len(X), quantiles=QUANTILES)
assert abs(ka_gt[0] - 1) < EPSILON
auc_gt = ka_gt.mean()
assert abs(ka_gt[1] - 0.97858424670806643) < EPSILON
assert abs(ka_gt[6] - 0.77621800324925161) < EPSILON
assert abs(auc_gt - 0.26515079212133352) < EPSILON
n_components = 10
ka_gv = kanalysis(X, Y_true, n_components=n_components, quantiles=QUANTILES)
assert abs(ka_gv[0] - 1) < EPSILON
assert (abs(ka_gv - ka_gt[:n_components + 1]) < EPSILON).all()
def test_sinc_n_components():
rng = np.random.RandomState(42)
X = rng.uniform(-1, 1, [100, 1])
Y_true = np.sign(np.cos(4. * np.pi * X))
ka_gt = kanalysis(X, Y_true, n_components=len(X), quantiles=QUANTILES)
assert abs(ka_gt[0] - 1) < EPSILON
auc_gt = ka_gt.mean()
assert abs(ka_gt[1] - 0.97858424670806643) < EPSILON
assert abs(ka_gt[6] - 0.77621800324925161) < EPSILON
assert abs(auc_gt - 0.26515079212133352) < EPSILON
n_components = 10
ka_gv = kanalysis(X,
Y_true,
n_components=n_components,
quantiles=QUANTILES)
assert abs(ka_gv[0] - 1) < EPSILON
assert (abs(ka_gv - ka_gt[:n_components + 1]) < EPSILON).all()
def test_sinc():
rng = np.random.RandomState(42)
X = rng.uniform(-1, 1, [100, 1])
Y_true = np.sign(np.cos(4. * np.pi * X))
ka = kanalysis(X, Y_true, quantiles=QUANTILES)
assert abs(ka[0] - 1) < EPSILON
auc = ka.mean()
assert abs(ka[1] - 0.97858424670806643) < EPSILON
assert abs(ka[6] - 0.77621800324925161) < EPSILON
assert abs(auc - 0.26515079212133352) < EPSILON
X2 = np.cos(4*np.pi*X)
ka2 = kanalysis(X2, Y_true, quantiles=QUANTILES)
assert abs(ka2[0] - 1) < EPSILON
auc2 = ka2.mean()
assert abs(ka2[2] - 0.18664740171388722) < EPSILON
assert abs(ka2[8] - 0.08675467229138914) < EPSILON
assert abs(auc2 - 0.054521533910044662) < EPSILON
def test_sinc():
rng = np.random.RandomState(42)
X = rng.uniform(-1, 1, [100, 1])
Y_true = np.sign(np.cos(4. * np.pi * X))
ka = kanalysis(X, Y_true, quantiles=QUANTILES)
assert abs(ka[0] - 1) < EPSILON
auc = ka.mean()
assert abs(ka[1] - 0.97858424670806643) < EPSILON
assert abs(ka[6] - 0.77621800324925161) < EPSILON
assert abs(auc - 0.26515079212133352) < EPSILON
X2 = np.cos(4 * np.pi * X)
ka2 = kanalysis(X2, Y_true, quantiles=QUANTILES)
assert abs(ka2[0] - 1) < EPSILON
auc2 = ka2.mean()
assert abs(ka2[2] - 0.18664740171388722) < EPSILON
assert abs(ka2[8] - 0.08675467229138914) < EPSILON
assert abs(auc2 - 0.054521533910044662) < EPSILON
def test_simple2d():
rng = np.random.RandomState(42)
X = rng.randn(8, 4)
Y = rng.randn(*X.shape)
gv = kanalysis(X, Y)
assert abs(gv[0] - 1) < EPSILON
gt = np.array([
1.00000000e+00, 8.36576988e-01, 7.62676568e-01,
6.14490305e-01, 3.61717470e-01, 2.32448139e-01,
1.12094204e-01, 9.04488924e-31, 7.08820460e-31,
])
assert (abs(gv - gt) < EPSILON).all()
def test_simple1d():
rng = np.random.RandomState(42)
X = rng.randn(8, 4)
Y = rng.randn(8)
gv = kanalysis(X, Y)
assert abs(gv[0] - 1) < EPSILON
gt = np.array([
1.00000000e+00, 9.71468057e-01, 5.59939220e-01,
5.20056617e-01, 5.04160095e-01, 3.38182408e-01,
3.12550170e-01, 1.50530684e-30, 1.03692068e-30,
])
assert (abs(gv - gt) < EPSILON).all()
def test_simple2d():
rng = np.random.RandomState(42)
X = rng.randn(8, 4)
Y = rng.randn(*X.shape)
gv = kanalysis(X, Y)
assert abs(gv[0] - 1) < EPSILON
gt = np.array([
1.00000000e+00,
8.36576988e-01,
7.62676568e-01,
6.14490305e-01,
3.61717470e-01,
2.32448139e-01,
1.12094204e-01,
9.04488924e-31,
7.08820460e-31,
])
assert (abs(gv - gt) < EPSILON).all()
def test_simple1d():
rng = np.random.RandomState(42)
X = rng.randn(8, 4)
Y = rng.randn(8)
gv = kanalysis(X, Y)
assert abs(gv[0] - 1) < EPSILON
gt = np.array([
1.00000000e+00,
9.71468057e-01,
5.59939220e-01,
5.20056617e-01,
5.04160095e-01,
3.38182408e-01,
3.12550170e-01,
1.50530684e-30,
1.03692068e-30,
])
assert (abs(gv - gt) < EPSILON).all()
def test_smoke_size():
rng = np.random.RandomState(42)
X = rng.randn(10, 4)
Y = rng.randn(*X.shape)
gv = kanalysis(X, Y)
assert gv.size == len(X) + 1
def test_smoke_size():
rng = np.random.RandomState(42)
X = rng.randn(10, 4)
Y = rng.randn(*X.shape)
gv = kanalysis(X, Y)
assert gv.size == len(X) + 1
