def test_estimate_for_monte_carlo(num_models, num_samples,
mc_sample_allocation):
mc_outputs = [np.empty(0) for _ in range(num_models)]
mc_outputs[0] = np.random.random(num_samples)
covariance = np.eye(num_models)
est = Estimator(mc_sample_allocation, covariance)
expected_estimate = np.mean(mc_outputs[0])
assert est.get_estimate(mc_outputs) == pytest.approx(expected_estimate)
def test_two_model_estimate():
compressed_allocation = np.array([[1, 1, 1, 1], [5, 1, 1, 0],
[10, 0, 0, 1]])
allocation = MLMCSampleAllocation(compressed_allocation)
model_outputs = [np.arange(1, 7), np.arange(1, 17)]
covariance = np.array([[1, 0.5], [0.5, 1]])
est = Estimator(allocation, covariance)
expected_estimate = 10.545454545454547
assert est.get_estimate(model_outputs) == pytest.approx(expected_estimate)