def test_conf_level_within_zero_one_bounds():
ee = [0, 0, 0]
N = 1
num_resamples = 2
conf_level_too_low = -1
compute_mu_star_confidence(ee, N, num_resamples, conf_level_too_low)
conf_level_too_high = 2
compute_mu_star_confidence(ee, N, num_resamples, conf_level_too_high)
def test_conf_level_within_zero_one_bounds():
ee = [0, 0, 0]
N = 1
num_resamples = 2
conf_level_too_low = -1
compute_mu_star_confidence(ee, N, num_resamples, conf_level_too_low)
conf_level_too_high = 2
compute_mu_star_confidence(ee, N, num_resamples, conf_level_too_high)
def test_compute_mu_star_confidence():
'''
Tests that compute mu_star_confidence is computed correctly
'''
ee = np.array([2.52, 2.01, 2.30, 0.66, 0.93, 1.3], dtype=np.float)
num_trajectories = 6
num_resamples = 1000
conf_level = 0.95
actual = compute_mu_star_confidence(ee, num_trajectories, num_resamples, conf_level)
expected = 0.5
assert_allclose(actual, expected, atol=1e-01)
def test_compute_mu_star_confidence():
'''
Tests that compute mu_star_confidence is computed correctly
'''
ee = np.array([2.52, 2.01, 2.30, 0.66, 0.93, 1.3], dtype=float)
num_trajectories = 6
num_resamples = 1000
conf_level = 0.95
actual = compute_mu_star_confidence(ee, num_trajectories, num_resamples,
conf_level)
expected = 0.5
assert_allclose(actual, expected, atol=1e-01)
