def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
    """Simulate some data drawn from a matern covariance and a periodic exponential for use in MRD demos."""
    Q_signal = 4
    import GPy
    import numpy as np
    np.random.seed(3000)

    k = GPy.kern.Matern32(Q_signal, 1., lengthscale=(np.random.uniform(1, 6, Q_signal)), ARD=1)
    for i in range(Q_signal):
        k += GPy.kern.PeriodicExponential(1, variance=1., active_dims=[i], period=3., lower=-2, upper=6)
    t = np.c_[[np.linspace(-1, 5, N) for _ in range(Q_signal)]].T
    K = k.K(t)
    s2, s1, s3, sS = np.random.multivariate_normal(np.zeros(K.shape[0]), K, size=(4))[:, :, None]

    Y1, Y2, Y3, S1, S2, S3 = _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS)

    slist = [sS, s1, s2, s3]
    slist_names = ["sS", "s1", "s2", "s3"]
    Ylist = [Y1, Y2, Y3]

    if plot_sim:
        from matplotlib import pyplot as plt
        import matplotlib.cm as cm
        import itertools
        fig = plt.figure("MRD Simulation Data", figsize=(8, 6))
        fig.clf()
        ax = fig.add_subplot(2, 1, 1)
        labls = slist_names
        for S, lab in itertools.izip(slist, labls):
            ax.plot(S, label=lab)
        ax.legend()
        for i, Y in enumerate(Ylist):
            ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
            ax.imshow(Y, aspect='auto', cmap=cm.gray)  # @UndefinedVariable
            ax.set_title("Y{}".format(i + 1))
        plt.draw()
        plt.tight_layout()

    return slist, [S1, S2, S3], Ylist
dataset/ETHPy150Open SheffieldML/GPy/GPy/examples/dimensionality_reduction.py/_simulate_matern/original