def test_sample(self):
initial_rng_state = np.random.get_state()
with self.SMC_test:
mtrace = pm.sample_smc(draws=self.samples,
return_inferencedata=False)
assert_random_state_equal(initial_rng_state, np.random.get_state())
x = mtrace["X"]
mu1d = np.abs(x).mean(axis=0)
np.testing.assert_allclose(self.muref, mu1d, rtol=0.0, atol=0.03)
def test_normal_model(self):
data = st.norm(10, 0.5).rvs(1000, random_state=self.get_random_state())
initial_rng_state = np.random.get_state()
with pm.Model() as m:
mu = pm.Normal("mu", 0, 3)
sigma = pm.HalfNormal("sigma", 1)
y = pm.Normal("y", mu, sigma, observed=data)
idata = pm.sample_smc(draws=2000, kernel=pm.smc.MH)
assert_random_state_equal(initial_rng_state, np.random.get_state())
post = idata.posterior.stack(sample=("chain", "draw"))
assert np.abs(post["mu"].mean() - 10) < 0.1
assert np.abs(post["sigma"].mean() - 0.5) < 0.05