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_aevb(self):
_, model, _ = models.exponential_beta()
x = model.x
y = model.y
mu = theano.shared(x.init_value) * 2
sd = theano.shared(x.init_value) * 3
with model:
inference = self.inference(local_rv={y: (mu, sd)})
inference.fit(3)
def test_multiple_replacements(inference_spec):
_, model, _ = models.exponential_beta(n=2)
x = model.x
y = model.y
xy = x * y
xpy = x + y
with model:
ap = inference_spec().approx
xy_, xpy_ = ap.apply_replacements([xy, xpy])
xy_s, xpy_s = ap.sample_node([xy, xpy])
xy_.eval()
xpy_.eval()
xy_s.eval()
xpy_s.eval()
def test_multiple_replacements(self):
_, model, _ = models.exponential_beta(n=2)
x = model.x
y = model.y
xy = x * y
xpy = x + y
with model:
mf = self.inference().approx
xy_, xpy_ = mf.apply_replacements([xy, xpy])
xy_s, xpy_s = mf.sample_node([xy, xpy])
xy_.eval()
xpy_.eval()
xy_s.eval()
xpy_s.eval()
def test_multiple_replacements(inference_spec):
_, model, _ = models.exponential_beta(n=2)
x = model.x
y = model.y
xy = x*y
xpy = x+y
with model:
ap = inference_spec().approx
xy_, xpy_ = ap.apply_replacements([xy, xpy])
xy_s, xpy_s = ap.sample_node([xy, xpy])
xy_.eval()
xpy_.eval()
xy_s.eval()
xpy_s.eval()
def test_multiple_replacements(self):
_, model, _ = models.exponential_beta(n=2)
x = model.x
y = model.y
xy = x*y
xpy = x+y
with model:
mf = self.inference().approx
xy_, xpy_ = mf.apply_replacements([xy, xpy])
xy_s, xpy_s = mf.sample_node([xy, xpy])
xy_.eval()
xpy_.eval()
xy_s.eval()
xpy_s.eval()
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)
def test_aevb(self):
_, model, _ = models.exponential_beta(n=2)
x = model.x
y = model.y
mu = theano.shared(x.init_value) * 2
rho = theano.shared(np.zeros_like(x.init_value))
with model:
inference = self.inference(local_rv={y: (mu, rho)})
approx = inference.fit(3, obj_n_mc=2)
approx.sample_vp(10)
approx.apply_replacements(y,
more_replacements={
x: np.asarray([1, 1],
dtype=x.dtype)
}).eval()
def test_aevb_histogram(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_vp(10000)
histogram = Histogram(trace0, local_rv={x: (mu, rho)})
trace1 = histogram.sample_vp(10000)
histogram.random(no_rand=True)
histogram.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)
