def bgplvm_simulation_matlab_compare():
from GPy.util.datasets import simulation_BGPLVM
sim_data = simulation_BGPLVM()
Y = sim_data['Y']
S = sim_data['S']
mu = sim_data['mu']
num_inducing, [_, Q] = 3, mu.shape
from GPy.models import mrd
from GPy import kern
reload(mrd); reload(kern)
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k,
# X=mu,
# X_variance=S,
_debug=False)
m.auto_scale_factor = True
m['noise'] = Y.var() / 100.
m['linear_variance'] = .01
return m
