

def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
    from GPy import kern
    from GPy.models import MRD
    (D1, D2, D3, N, num_inducing, Q) = (60, 20, 36, 60, 6, 5)
    (_, _, Ylist) = _simulate_sincos(D1, m, D3, N, num_inducing, plot_sim)
    k = (kern.Linear(Q, ARD=True) + kern.White(Q, variance=0.0001))
    m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing, kernel=k, initx='PCA_concat', initz='permute', **kw)
    m['.*noise'] = [(Y.var() / 40.0) for Y in Ylist]
    if optimize:
        print('Optimizing Model:')
        m.optimize(messages=verbose, max_iters=8000.0)
    if plot:
        m.X.plot('MRD Latent Space 1D')
        m.plot_scales('MRD Scales')
    return m

dataset/ETHPy150Open SheffieldML/GPy/GPy/examples/dimensionality_reduction.py mrd_simulation/VarMisuse@79/97 `D2`->`m`