def func(chol_vec, delta):
chol = at.stack([
at.stack([at.exp(0.1 * chol_vec[0]), 0]),
at.stack([chol_vec[1], 2 * at.exp(chol_vec[2])]),
])
cov = at.dot(chol, chol.T)
return MvNormalLogp()(cov, delta)
def test_hessian(self):
chol_vec = at.vector("chol_vec")
chol_vec.tag.test_value = floatX(np.array([0.1, 2, 3]))
chol = at.stack([
at.stack([at.exp(0.1 * chol_vec[0]), 0]),
at.stack([chol_vec[1], 2 * at.exp(chol_vec[2])]),
])
cov = at.dot(chol, chol.T)
delta = at.matrix("delta")
delta.tag.test_value = floatX(np.ones((5, 2)))
logp = MvNormalLogp()(cov, delta)
g_cov, g_delta = at.grad(logp, [cov, delta])
# TODO: What's the test? Something needs to be asserted.
at.grad(g_delta.sum() + g_cov.sum(), [delta, cov])
def test_logp(self):
np.random.seed(42)
chol_val = floatX(np.array([[1, 0.9], [0, 2]]))
cov_val = floatX(np.dot(chol_val, chol_val.T))
cov = at.matrix("cov")
cov.tag.test_value = cov_val
delta_val = floatX(np.random.randn(5, 2))
delta = at.matrix("delta")
delta.tag.test_value = delta_val
expect = stats.multivariate_normal(mean=np.zeros(2), cov=cov_val)
expect = expect.logpdf(delta_val).sum()
logp = MvNormalLogp()(cov, delta)
logp_f = aesara.function([cov, delta], logp)
logp = logp_f(cov_val, delta_val)
npt.assert_allclose(logp, expect)