def test_advi_optimizer():
n = 1000
sd0 = 2.
mu0 = 4.
sd = 3.
mu = -5.
data = sd * np.random.RandomState(0).randn(n) + mu
d = n / sd**2 + 1 / sd0**2
mu_post = (n * np.mean(data) / sd**2 + mu0 / sd0**2) / d
with Model() as model:
mu_ = Normal('mu', mu=mu0, sd=sd0, testval=0)
Normal('x', mu=mu_, sd=sd, observed=data)
optimizer = adagrad_optimizer(learning_rate=0.1, epsilon=0.1)
advi_fit = advi(model=model, n=1000, optimizer=optimizer, random_seed=1)
np.testing.assert_allclose(advi_fit.means['mu'], mu_post, rtol=0.1)
trace = sample_vp(advi_fit, 10000, model)
np.testing.assert_allclose(np.mean(trace['mu']), mu_post, rtol=0.4)
np.testing.assert_allclose(np.std(trace['mu']), np.sqrt(1. / d), rtol=0.4)
def test_advi_optimizer():
n = 1000
sd0 = 2.
mu0 = 4.
sd = 3.
mu = -5.
data = sd * np.random.RandomState(0).randn(n) + mu
d = n / sd**2 + 1 / sd0**2
mu_post = (n * np.mean(data) / sd**2 + mu0 / sd0**2) / d
with Model() as model:
mu_ = Normal('mu', mu=mu0, sd=sd0, testval=0)
Normal('x', mu=mu_, sd=sd, observed=data)
optimizer = adagrad_optimizer(learning_rate=0.1, epsilon=0.1)
advi_fit = advi(model=model, n=1000, optimizer=optimizer, random_seed=1)
np.testing.assert_allclose(advi_fit.means['mu'], mu_post, rtol=0.1)
trace = sample_vp(advi_fit, 10000, model)
np.testing.assert_allclose(np.mean(trace['mu']), mu_post, rtol=0.4)
np.testing.assert_allclose(np.std(trace['mu']), np.sqrt(1. / d), rtol=0.4)
