validate.py 文件源码

def analysis(N, M, I, L, sqrt=False):
    """
    Conduct a Kalman filter validation experiment. Output results
    (concerning the error, i.e., x_hat - x) for the last time step
    only. If *sqrt* use the square root form Kalman filter.
    """
    sim = setup_random_test(N, M, I, L)
    if sqrt:
        post = sqrt_kf_sim(sim)
    else:
        post = kf_sim(sim)
    # output statistics of \hat{x}_{I|I}
    error_I = []
    for l in range(sim['L']):
        error_I.append(post[l]['error'][-1])
    E_I = NP.stack(error_I, 1)
    E_I_mean = NP.mean(E_I, 1)
    P_I = NP.cov(E_I)
    print('Mean of error at time step I={}'.format(I))
    for E_I_mean_n in E_I_mean:
        print('{:9.2e}'.format(E_I_mean_n))
    print('')
    print('True posterior covariance at time step I')
    print(NP.array_str(post['P'][-1], precision=2))
    print('')
    print('Empirical posterior covariance at time step I')
    print(NP.array_str(P_I, precision=2))
    return sim, post