def test_CovUnconstrainedCholesky(seeded_rng):
cov = CovUnconstrainedCholesky(size=m)
L = cov.L.numpy()
cov_np = L @ L.T
logdet_np, sinv_np, sinvx_np = logdet_sinv_np(X, cov_np)
assert_allclose(logdet_np, cov.logdet, rtol=rtol)
assert_allclose(sinv_np, cov.solve(eye), rtol=rtol)
assert_allclose(sinvx_np, cov.solve(X_tf), rtol=rtol)
def test_CovConstant(seeded_rng):
cov_np = wishart.rvs(df=m + 2, scale=np.eye(m))
cov = CovUnconstrainedCholesky(Sigma=cov_np)
# verify what we pass is what we get
cov_tf = cov._cov
assert_allclose(cov_tf, cov_np)
# compute the naive version
logdet_np, sinv_np, sinvx_np = logdet_sinv_np(X, cov_np)
assert_allclose(logdet_np, cov.logdet, rtol=rtol)
assert_allclose(sinv_np, cov.solve(eye), rtol=rtol)
assert_allclose(sinvx_np, cov.solve(X_tf), rtol=rtol)