def test_lds_mean(T=25, D=4):
"""
Test lds_mean correctness
"""
As, bs, Qi_sqrts, ms, Ri_sqrts = make_lds_parameters(T, D)
J_diag, J_lower_diag, h = convert_lds_to_block_tridiag(As, bs, Qi_sqrts, ms, Ri_sqrts)
# Convert to dense matrix
J_full = np.zeros((T*D, T*D))
for t in range(T):
J_full[t*D:(t+1)*D, t*D:(t+1)*D] = J_diag[t]
for t in range(T-1):
J_full[t*D:(t+1)*D, (t+1)*D:(t+2)*D] = J_lower_diag[t].T
J_full[(t+1)*D:(t+2)*D, t*D:(t+1)*D] = J_lower_diag[t]
Sigma = np.linalg.inv(J_full)
mu_true = Sigma.dot(h.ravel()).reshape((T, D))
# Solve with the banded solver
mu_test = lds_mean(As, bs, Qi_sqrts, ms, Ri_sqrts)
assert np.allclose(mu_true, mu_test)
def mean(self):
return [lds_mean(*prms) for prms in self.params]
