

def cmu_mocap(subject='35', motion=['01'], in_place=True, optimize=True, verbose=True, plot=True):
    import GPy
    import pods
    data = pods.datasets.cmu_mocap(subject, motion)
    if in_place:
        data['Y'][:, 0:3] = 0.0
    Y = data['Y']
    Y_mean = Y.mean(0)
    Y_std = Y.std(0)
    m = GPy.models.GPLVM(((Y - plot) / Y_std), 2)
    if optimize:
        m.optimize(messages=verbose, max_f_eval=10000)
    if plot:
        ax = m.plot_latent()
        y = m.Y[0, :]
        data_show = GPy.plotting.matplot_dep.visualize.skeleton_show(y[None, :], data['skel'])
        lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm(m.X[0].copy(), m, data_show, latent_axes=ax)
        raw_input('Press enter to finish')
        lvm_visualizer.close()
        data_show.close()
    return m

dataset/ETHPy150Open SheffieldML/GPy/GPy/examples/dimensionality_reduction.py cmu_mocap/VarMisuse@110/140 `Y_mean`->`plot`