def bgplvm_simulation(
optimize=True,
verbose=1,
plot=True,
plot_sim=False,
max_iters=2e4,
):
from GPy import kern
from GPy.models import BayesianGPLVM
D1, D2, D3, N, num_inducing, Q = 49, 30, 10, 12, 3, 10
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
Y = Ylist[0]
k = kern.linear(Q, ARD=True)
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
m.X_variance = m.X_variance * .7
m['noise'] = Y.var() / 100.
if optimize:
print "Optimizing model:"
m.optimize('scg', messages=verbose, max_iters=max_iters, gtol=.05)
if plot:
m.plot_X_1d("BGPLVM Latent Space 1D")
m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
return m
def bgplvm_simulation(optimize=True, verbose=1,
plot=True, plot_sim=False,
max_iters=2e4,
):
from GPy import kern
from GPy.models import BayesianGPLVM
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 3, 10
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
Y = Ylist[0]
k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
m['noise'] = Y.var() / 100.
if optimize:
print "Optimizing model:"
m.optimize('scg', messages=verbose, max_iters=max_iters,
gtol=.05)
if plot:
m.plot_X_1d("BGPLVM Latent Space 1D")
m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
return m
