def test_transform_04(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 60).reshape(15, 4)
outputs = np.random.uniform(0, 5, 15)
ss = KernelActiveSubspaces(dim=2, method='local', n_boot=49)
ss.fit(inputs=inputs, outputs=outputs)
inactive = ss.transform(np.random.uniform(-1, 1, 8).reshape(2, 4))[1]
true_inactive = np.array([[0.27110018, -0.29359021],
[0.76399199, -0.02233936]])
np.testing.assert_array_almost_equal(true_inactive, inactive)
def test_transform_03(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 60).reshape(15, 4)
outputs = np.random.uniform(0, 5, 45).reshape(15, 3)
ss = KernelActiveSubspaces(dim=2, method='local', n_boot=50)
ss.fit(inputs=inputs, outputs=outputs, metric=np.diag(np.ones(3)))
active = ss.transform(np.random.uniform(-1, 1, 8).reshape(2, 4))[0]
true_active = np.array([[-0.18946138, 0.31916713],
[-0.25310859, -0.30280365]])
np.testing.assert_array_almost_equal(true_active, active)
def test_transform_01(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 30).reshape(15, 1, 2)
inputs = np.random.uniform(-1, 1, 30).reshape(15, 2)
weights = np.ones((15, 1)) / 15
ss = KernelActiveSubspaces(dim=2, n_features=4, n_boot=49)
ss.fit(inputs=inputs, gradients=gradients, weights=weights)
active = ss.transform(np.random.uniform(-1, 1, 4).reshape(2, 2))[0]
true_active = np.array([[0.94893046, 0.01774345],
[1.09617095, -0.20832091]])
np.testing.assert_array_almost_equal(true_active, active)
def test_transform_02(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 30).reshape(15, 1, 2)
inputs = np.random.uniform(-1, 1, 30).reshape(15, 2)
weights = np.ones((15, 1)) / 15
ss = KernelActiveSubspaces(dim=2,
n_features=4,
method='exact',
n_boot=49)
ss.fit(inputs=inputs, gradients=gradients, weights=weights)
inactive = ss.transform(np.random.uniform(-1, 1, 4).reshape(2, 2))[1]
true_inactive = np.array([[-0.33551797, 0.36254188],
[-0.19427817, 0.2576207]])
np.testing.assert_array_almost_equal(true_inactive, inactive)
