def test_cross_validation_run_01(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 15).reshape(5, -1)
outputs = np.random.uniform(0, 5, 10).reshape(5, -1)
gradients = np.random.uniform(-1, 1, 30).reshape(5, 2, 3)
ss = ActiveSubspaces(dim=1, method='exact')
csv = CrossValidation(inputs=inputs,
outputs=outputs,
gradients=gradients,
folds=2,
subspace=ss)
true_value = (8.91180601306311, 6.806790947903309)
np.testing.assert_array_almost_equal(csv.run(), true_value)
def test_cross_validation_run_01(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 15).reshape(5, -1)
outputs = np.random.uniform(0, 5, 10).reshape(5, -1)
gradients = np.random.uniform(-1, 1, 30).reshape(5, 2, 3)
ss = ActiveSubspaces()
csv = CrossValidation(inputs=inputs,
outputs=outputs,
gradients=gradients,
folds=2,
subspace=ss)
true_value = (8.186941403385733, 6.081926389368339)
np.testing.assert_array_almost_equal(csv.run(), true_value)
def test_cross_validation_run_02(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 10).reshape(5, 2)
outputs = np.random.uniform(0, 5, 10).reshape(5, 2)
gradients = np.random.uniform(-1, 1, 20).reshape(5, 2, 2)
fm = FeatureMap(distr='laplace',
bias=np.random.uniform(-1, 1, 3),
input_dim=2,
n_features=3,
params=np.zeros(1),
sigma_f=outputs.var())
ss = KernelActiveSubspaces(dim=1, feature_map=fm)
csv = CrossValidation(inputs=inputs,
outputs=outputs,
gradients=gradients,
folds=2,
subspace=ss)
true_value = (2.26333743325053, 0.43902733603381605)
np.testing.assert_array_almost_equal(csv.run(), true_value)