def test_get_Z(self):
Mt = SurrogateModel(d)
Mt.trans_p = lambda p: p
z = (3. + np.arange(Dx + Dp))[None, :]
x = z[:, :Dx]
p = z[:, Dx:]
with pytest.raises(AssertionError):
Z = Mt.get_Z(x)
Z = Mt.get_Z(x, p)
assert np.all(Z == z)
Mt.pmean = p[0]
Z = Mt.get_Z(x)
assert np.all(Z == z)
def test_prediction(self):
# Nothing given
Mt = SurrogateModel(d)
with pytest.raises(AssertionError):
M, S = Mt.predict(X)
# Give mean
Mt.pmean = np.random.uniform(*p_bounds.T)
with pytest.raises(AssertionError):
M, S = Mt.predict(X)
# Give training data
Mt.set_training_data(Z, Y)
with pytest.raises(AssertionError):
M, S = Mt.predict(X)
# Give hyperparameters
Mt.hyp = [0] * Mt.num_outputs
with pytest.raises(NotImplementedError):
M, S = Mt.predict(X)