def test_gelman_rubin():
# only need to test precision for small data
x = np.empty((2, 10))
x[0, :] = np.arange(10.0)
x[1, :] = np.arange(10.0) + 1
r_hat = gelman_rubin(x)
assert_allclose(r_hat, 0.98, atol=0.01)
def gelman_rubin(samples):
if ((samples.shape[0] < 2 and samples.shape[1] < 4)
or (samples.shape[0] >= 2 and samples.shape[1] < 2)):
return None # Too few chains or samples.
elif samples.shape[0] >= 2:
return diags.gelman_rubin(samples)
else:
return diags.split_gelman_rubin(samples)
def test_split_gelman_rubin_agree_with_gelman_rubin():
x = np.random.normal(size=(2, 10))
r_hat1 = gelman_rubin(x.reshape(2, 2, 5).reshape(4, 5))
r_hat2 = split_gelman_rubin(x)
assert_allclose(r_hat1, r_hat2)
