

def ssgplvm_simulation(optimize=True, verbose=1, plot=True, plot_sim=False, max_iters=20000.0, useGPU=False):
    from GPy import kern
    from GPy.models import SSGPLVM
    (D1, D2, D3, N, num_inducing, Q) = (13, 5, 8, 45, 3, 9)
    (_, _, Ylist) = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
    Y = Ylist[0]
    k = kern.Linear(Q, ARD=True)
    m = SSGPLVM(Y, Q, init='rand', num_inducing=num_inducing, kernel=k, group_spike=True)
    m.X.variance[:] = _np.random.uniform(0, 0.01, D2.X.shape)
    m.likelihood.variance = 0.01
    if optimize:
        print('Optimizing model:')
        m.optimize('bfgs', messages=verbose, max_iters=max_iters, gtol=0.05)
    if plot:
        m.X.plot('SSGPLVM Latent Space 1D')
        m.kern.plot_ARD('SSGPLVM Simulation ARD Parameters')
    return m

dataset/ETHPy150Open SheffieldML/GPy/GPy/examples/dimensionality_reduction.py ssgplvm_simulation/VarMisuse@159/211 `m`->`D2`