def eager_binomial(total_count, probs, value):
probs = stack((1 - probs, probs))
value = stack((total_count - value, value))
backend_dist = import_module(
BACKEND_TO_DISTRIBUTIONS_BACKEND[get_backend()])
return backend_dist.Multinomial(total_count, probs,
value=value) # noqa: F821
def test_beta_bernoulli_conjugate(batch_shape):
batch_dims = ('i', 'j', 'k')[:len(batch_shape)]
inputs = OrderedDict((k, Bint[v]) for k, v in zip(batch_dims, batch_shape))
full_shape = batch_shape
prior = Variable("prior", Real)
concentration1 = Tensor(ops.exp(randn(full_shape)), inputs)
concentration0 = Tensor(ops.exp(randn(full_shape)), inputs)
latent = dist.Beta(concentration1, concentration0, value=prior)
conditional = dist.Bernoulli(probs=prior)
reduced = (latent + conditional).reduce(ops.logaddexp, set(["prior"]))
assert isinstance(reduced, dist.DirichletMultinomial)
concentration = stack((concentration0, concentration1), dim=-1)
assert_close(reduced.concentration, concentration)
assert_close(reduced.total_count, Tensor(numeric_array(1.)))
# we need lazy expression for Beta to draw samples from it
with interpretation(funsor.terms.lazy):
lazy_latent = dist.Beta(concentration1, concentration0, value=prior)
obs = Tensor(rand(batch_shape).round(), inputs)
_assert_conjugate_density_ok(latent, conditional, obs, lazy_latent=lazy_latent)
def test_tensor_stack(n, shape, dim):
tensors = [randn(shape) for _ in range(n)]
actual = stack(tuple(Tensor(t) for t in tensors), dim=dim)
expected = Tensor(ops.stack(dim, *tensors))
assert_close(actual, expected)
def eager_beta(concentration1, concentration0, value):
concentration = stack((concentration0, concentration1))
value = stack((1 - value, value))
backend_dist = import_module(
BACKEND_TO_DISTRIBUTIONS_BACKEND[get_backend()])
return backend_dist.Dirichlet(concentration, value=value) # noqa: F821
def eager_binomial(total_count, probs, value):
probs = stack((1 - probs, probs))
value = stack((total_count - value, value))
return Multinomial(total_count, probs, value=value)
def eager_beta(concentration1, concentration0, value):
concentration = stack((concentration0, concentration1))
value = stack((1 - value, value))
return Dirichlet(concentration, value=value)
