def test_elbo():
mu0 = 1.5
sigma = 1.0
y_obs = np.array([1.6, 1.4])
post_mu = np.array([1.88], dtype=theano.config.floatX)
post_sd = np.array([1], dtype=theano.config.floatX)
# Create a model for test
with Model() as model:
mu = Normal('mu', mu=mu0, sd=sigma)
Normal('y', mu=mu, sd=1, observed=y_obs)
# Create variational gradient tensor
mean_field = MeanField(model=model)
elbo = -KL(mean_field)()(10000)
mean_field.shared_params['mu'].set_value(post_mu)
mean_field.shared_params['rho'].set_value(np.log(np.exp(post_sd) - 1))
f = theano.function([], elbo)
elbo_mc = f()
# Exact value
elbo_true = (-0.5 * (3 + 3 * post_mu**2 - 2 *
(y_obs[0] + y_obs[1] + mu0) * post_mu + y_obs[0]**2 +
y_obs[1]**2 + mu0**2 + 3 * np.log(2 * np.pi)) + 0.5 *
(np.log(2 * np.pi) + 1))
np.testing.assert_allclose(elbo_mc, elbo_true, rtol=0, atol=1e-1)
def from_full_rank(cls, full_rank):
"""
Construct FullRankADVI from FullRank approximation
Parameters
----------
full_rank : :class:`FullRank`
approximation to start with
Returns
-------
:class:`FullRankADVI`
"""
if not isinstance(full_rank, FullRank):
raise TypeError('Expected MeanField, got %r' % full_rank)
inference = object.__new__(cls)
objective = KL(full_rank)(None)
inference.hist = np.asarray(())
inference.objective = objective
return inference
def from_mean_field(cls, mean_field):
"""
Construct ADVI from MeanField approximation
Parameters
----------
mean_field : :class:`MeanField`
approximation to start with
Returns
-------
:class:`ADVI`
"""
if not isinstance(mean_field, MeanField):
raise TypeError('Expected MeanField, got %r' % mean_field)
inference = object.__new__(cls)
objective = KL(mean_field)(None)
inference.hist = np.asarray(())
inference.objective = objective
return inference
def from_mean_field(cls, mean_field, gpu_compat=False):
"""
Construct FullRankADVI from MeanField approximation
Parameters
----------
mean_field : MeanField
approximation to start with
Flags
-----
gpu_compat : bool
use GPU compatible version or not
Returns
-------
FullRankADVI
"""
full_rank = FullRank.from_mean_field(mean_field, gpu_compat)
inference = object.__new__(cls)
objective = KL(full_rank)(None)
inference.objective = objective
inference.hist = np.asarray(())
return inference
