def bgplvm_simulation(optimize='scg',
plot=True,
max_f_eval=2e4):
# from GPy.core.transformations import logexp_clipped
D1, D2, D3, N, num_inducing, Q = 15, 8, 8, 100, 3, 5
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot)
from GPy.models import mrd
from GPy import kern
reload(mrd); reload(kern)
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, _debug=True)
# m.constrain('variance|noise', logexp_clipped())
m['noise'] = Y.var() / 100.
m['linear_variance'] = .01
if optimize:
print "Optimizing model:"
m.optimize(optimize, max_iters=max_f_eval,
max_f_eval=max_f_eval,
messages=True, gtol=.05)
if plot:
m.plot_X_1d("BGPLVM Latent Space 1D")
m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
return m
def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
D1, D2, D3, N, num_inducing, Q = 150, 200, 400, 500, 3, 7
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
from GPy.models import mrd
from GPy import kern
reload(mrd); reload(kern)
k = kern.linear(Q, [.05] * Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
m = mrd.MRD(Ylist, input_dim=Q, num_inducing=num_inducing, kernels=k, initx="", initz='permute', **kw)
for i, Y in enumerate(Ylist):
m['{}_noise'.format(i + 1)] = Y.var() / 100.
# DEBUG
# np.seterr("raise")
if optimize:
print "Optimizing Model:"
m.optimize(messages=1, max_iters=8e3, max_f_eval=8e3, gtol=.1)
if plot:
m.plot_X_1d("MRD Latent Space 1D")
m.plot_scales("MRD Scales")
return m
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
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
def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
from GPy import kern
from GPy.models import MRD
from GPy.likelihoods import Gaussian
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
likelihood_list = [Gaussian(x, normalize=True) for x in Ylist]
k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2))
m = MRD(likelihood_list, input_dim=Q, num_inducing=num_inducing, kernels=k, initx="", initz='permute', **kw)
m.ensure_default_constraints()
for i, bgplvm in enumerate(m.bgplvms):
m['{}_noise'.format(i)] = bgplvm.likelihood.Y.var() / 500.
if optimize:
print "Optimizing Model:"
m.optimize(messages=verbose, max_iters=8e3, gtol=.1)
if plot:
m.plot_X_1d("MRD Latent Space 1D")
m.plot_scales("MRD Scales")
return m
