def test_bernoulliprobs_sample(batch_shape, sample_inputs):
batch_dims = ('i', 'j', 'k')[:len(batch_shape)]
inputs = OrderedDict((k, bint(v)) for k, v in zip(batch_dims, batch_shape))
probs = Tensor(torch.rand(batch_shape), inputs)
funsor_dist = dist.Bernoulli(probs=probs)
_check_sample(funsor_dist,
sample_inputs,
inputs,
atol=5e-2,
num_samples=100000)
def loss_function(data, subsample_scale):
# Lazily sample from the guide.
loc, scale = encode(data)
q = funsor.Independent(dist.Normal(loc['i'], scale['i'], value='z_i'),
'z', 'i', 'z_i')
# Evaluate the model likelihood at the lazy value z.
probs = decode('z')
p = dist.Bernoulli(probs['x', 'y'], value=data['x', 'y'])
p = p.reduce(ops.add, {'x', 'y'})
# Construct an elbo. This is where sampling happens.
elbo = funsor.Integrate(q, p - q, frozenset(['z']))
elbo = elbo.reduce(ops.add, 'batch') * subsample_scale
loss = -elbo
return loss
def test_bernoulli_logits_density(batch_shape, syntax):
batch_dims = ('i', 'j', 'k')[:len(batch_shape)]
inputs = OrderedDict((k, bint(v)) for k, v in zip(batch_dims, batch_shape))
@funsor.torch.function(reals(), reals(), reals())
def bernoulli(logits, value):
return torch.distributions.Bernoulli(logits=logits).log_prob(value)
check_funsor(bernoulli, {'logits': reals(), 'value': reals()}, reals())
logits = Tensor(torch.rand(batch_shape), inputs)
value = Tensor(torch.rand(batch_shape).round(), inputs)
expected = bernoulli(logits, value)
check_funsor(expected, inputs, reals())
d = Variable('value', reals())
if syntax == 'eager':
actual = dist.BernoulliLogits(logits, value)
elif syntax == 'lazy':
actual = dist.BernoulliLogits(logits, d)(value=value)
elif syntax == 'generic':
actual = dist.Bernoulli(logits=logits)(value=value)
check_funsor(actual, inputs, reals())
assert_close(actual, expected)