def make_synthetic_data():
    mu_init = np.zeros(D)
    # mu_init[0] = 1.0
    sigma_init = 0.5*np.eye(D)

    A = np.eye(D)
    # A[:2,:2] = \
    #     0.99*np.array([[np.cos(np.pi/24), -np.sin(np.pi/24)],
    #                    [np.sin(np.pi/24),  np.cos(np.pi/24)]])
    sigma_states = 0.1*np.eye(D)

    # C = np.hstack((np.ones((K-1, 1)), np.zeros((K-1, D-1))))
    C = 0. * np.random.randn(K-1, D)

    truemodel = MultinomialLDS(K, D,
        init_dynamics_distn=Gaussian(mu=mu_init,sigma=sigma_init),
        dynamics_distn=AutoRegression(A=A,sigma=sigma_states),
        C=C
        )

    data_list = []
    Xs = []
    for i in xrange(Ndata):
        data = truemodel.generate(T=T, N=N)
        data_list.append(data)
        Xs.append(data["x"])
    return data_list, Xs

# Inference
dataset/ETHPy150Open HIPS/pgmult/examples/multinomial_lds_2.py/make_synthetic_data/original