def test_welford_covariance(jitted, diagonal, regularize):
with optional(jitted,
disable_jit()), optional(jitted,
control_flow_prims_disabled()):
np.random.seed(0)
loc = np.random.randn(3)
a = np.random.randn(3, 3)
target_cov = np.matmul(a, a.T)
x = np.random.multivariate_normal(loc, target_cov, size=(2000, ))
x = device_put(x)
@jit
def get_cov(x):
wc_init, wc_update, wc_final = welford_covariance(
diagonal=diagonal)
wc_state = wc_init(3)
wc_state = fori_loop(0, 2000, lambda i, val: wc_update(x[i], val),
wc_state)
cov, cov_inv_sqrt = wc_final(wc_state, regularize=regularize)
return cov, cov_inv_sqrt
cov, cov_inv_sqrt = get_cov(x)
if diagonal:
diag_cov = jnp.diagonal(target_cov)
assert_allclose(cov, diag_cov, rtol=0.06)
assert_allclose(cov_inv_sqrt,
jnp.sqrt(jnp.reciprocal(diag_cov)),
rtol=0.06)
else:
assert_allclose(cov, target_cov, rtol=0.06)
assert_allclose(cov_inv_sqrt,
jnp.linalg.cholesky(jnp.linalg.inv(cov)),
rtol=0.06)
def test_tscan(prims_disabled):
def f(tree, yz):
y, z = yz
return tree_map(lambda x: (x + y) * z, tree)
Tree = laxtuple("Tree", ["x", "y", "z"])
a = Tree(np.array([1., 2.]),
np.array(3., dtype=np.float32),
np.array(4., dtype=np.float32))
bs = (np.array([1., 2., 3., 4.]),
np.array([4., 3., 2., 1.]))
expected_tree = lax.scan(f, a, bs)
with optional(prims_disabled, control_flow_prims_disabled()):
actual_tree = tscan(f, a, bs, fields=(0, 2))
assert_allclose(actual_tree.x, expected_tree.x)
assert_allclose(actual_tree.z, expected_tree.z)
assert actual_tree.y is None
def model(data):
x = numpyro.sample('x', dist.Normal(0, 1))
with optional(use_context_manager, handlers.scale(scale=10)):
numpyro.sample('obs', dist.Normal(x, 1), obs=data)
