def test_aevb_empirical(self):
_, model, _ = models.exponential_beta(n=2)
x = model.x
mu = theano.shared(x.init_value)
rho = theano.shared(np.zeros_like(x.init_value))
with model:
inference = ADVI(local_rv={x: (mu, rho)})
approx = inference.approx
trace0 = approx.sample(10000)
approx = Empirical(trace0, local_rv={x: (mu, rho)})
trace1 = approx.sample(10000)
approx.random(no_rand=True)
approx.random_fn(no_rand=True)
np.testing.assert_allclose(trace0['y'].mean(0),
trace1['y'].mean(0),
atol=0.02)
np.testing.assert_allclose(trace0['y'].var(0),
trace1['y'].var(0),
atol=0.02)
np.testing.assert_allclose(trace0['x'].mean(0),
trace1['x'].mean(0),
atol=0.02)
np.testing.assert_allclose(trace0['x'].var(0),
trace1['x'].var(0),
atol=0.02)
def test_sampling(self):
with models.multidimensional_model()[1]:
full_rank = FullRankADVI()
approx = full_rank.fit(20)
trace0 = approx.sample(10000)
approx = Empirical(trace0)
trace1 = approx.sample(100000)
np.testing.assert_allclose(trace0['x'].mean(0), trace1['x'].mean(0), atol=0.01)
np.testing.assert_allclose(trace0['x'].var(0), trace1['x'].var(0), atol=0.01)
def test_aevb_empirical():
_, model, _ = models.exponential_beta(n=2)
x = model.x
mu = theano.shared(x.init_value)
rho = theano.shared(np.zeros_like(x.init_value))
with model:
inference = ADVI(local_rv={x: (mu, rho)})
approx = inference.approx
trace0 = approx.sample(10000)
approx = Empirical(trace0, local_rv={x: (mu, rho)})
trace1 = approx.sample(10000)
np.testing.assert_allclose(trace0['y'].mean(0), trace1['y'].mean(0), atol=0.02)
np.testing.assert_allclose(trace0['y'].var(0), trace1['y'].var(0), atol=0.02)
np.testing.assert_allclose(trace0['x'].mean(0), trace1['x'].mean(0), atol=0.02)
np.testing.assert_allclose(trace0['x'].var(0), trace1['x'].var(0), atol=0.02)