def fit_lds_model(Xs, Xtest, N_samples=100):
    model = MultinomialLDS(K, D,
        init_dynamics_distn=Gaussian(mu_0=np.zeros(D), sigma_0=np.eye(D), kappa_0=1.0, nu_0=D+1.0),
        dynamics_distn=AutoRegression(nu_0=D*1,S_0=np.eye(D),M_0=np.zeros((D,D)),K_0=np.eye(D)),
        sigma_C=1
        )

    for X in Xs:
        model.add_data(X)
    data = model.data_list[0]

    samples = []
    lls = []
    test_lls = []
    mc_test_lls = []
    pis = []
    psis = []
    zs = []
    timestamps = [time.time()]
    for smpl in progprint_xrange(N_samples):
        model.resample_model()
        timestamps.append(time.time())

        samples.append(model.copy_sample())
        # TODO: Use log_likelihood() to marginalize over z
        lls.append(model.log_likelihood())
        # test_lls.append(model.heldout_log_likelihood(Xtest, M=50)[0])
        mc_test_lls.append(model._mc_heldout_log_likelihood(Xtest, M=1)[0])
        pis.append(model.pi(data))
        psis.append(model.psi(data))
        zs.append(data["states"].stateseq)

    lls = np.array(lls)
    test_lls = np.array(test_lls)
    pis = np.array(pis)
    psis = np.array(psis)
    zs = np.array(zs)
    timestamps = np.array(timestamps)
    timestamps -= timestamps[0]
    return model, lls, test_lls, mc_test_lls, pis, psis, zs, timestamps
dataset/ETHPy150Open HIPS/pgmult/examples/multinomial_lds_2.py/fit_lds_model/operator `+` -> `*`