def test_reparametrize_02(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 6).reshape(3, 1, 2)
inputs = np.random.uniform(-1, 1, 6).reshape(3, 2)
fm = FeatureMap(distr='multivariate_normal',
bias=np.ones((1, 2)),
input_dim=inputs.shape[1],
n_features=2,
params=np.ones(inputs.shape[1]),
sigma_f=1)
ss = KernelActiveSubspaces(dim=2, feature_map=fm)
features = ss._reparametrize(inputs, gradients)[1]
true_features = np.array([[-0.2391454, 0.48143467],
[0.42589822, 0.75674833],
[-0.37950285, 0.53470539]])
np.testing.assert_array_almost_equal(features, true_features)
def test_reparametrize_01(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 6).reshape(3, 1, 2)
inputs = np.random.uniform(-1, 1, 6).reshape(3, 2)
fm = FeatureMap(distr='multivariate_normal',
bias=np.ones((1, 2)),
input_dim=inputs.shape[1],
n_features=2,
params=np.ones(inputs.shape[1]),
sigma_f=1)
ss = KernelActiveSubspaces(dim=2, feature_map=fm)
pseudo_gradients = ss._reparametrize(inputs, gradients)[0]
true_pseudo_gradients = np.array([[[-1.17123517, 0.69671587]],
[[0.29308353, 1.12067323]],
[[-0.0036059, -1.75277398]]])
np.testing.assert_array_almost_equal(pseudo_gradients,
true_pseudo_gradients)
