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 swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False):
from GPy.util.datasets import swiss_roll_generated
from GPy.core.transformations import logexp_clipped
data = swiss_roll_generated(N=N, sigma=sigma)
Y = data['Y']
Y -= Y.mean()
Y /= Y.std()
t = data['t']
c = data['colors']
try:
from sklearn.manifold.isomap import Isomap
iso = Isomap().fit(Y)
X = iso.embedding_
if Q > 2:
X = np.hstack((X, np.random.randn(N, Q - 2)))
except ImportError:
X = np.random.randn(N, Q)
if plot:
from mpl_toolkits import mplot3d
import pylab
fig = pylab.figure("Swiss Roll Data")
ax = fig.add_subplot(121, projection='3d')
ax.scatter(*Y.T, c=c)
ax.set_title("Swiss Roll")
ax = fig.add_subplot(122)
ax.scatter(*X.T[:2], c=c)
ax.set_title("Initialization")
var = .5
S = (var * np.ones_like(X) + np.clip(np.random.randn(N, Q) * var ** 2,
- (1 - var),
(1 - var))) + .001
Z = np.random.permutation(X)[:num_inducing]
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
m = BayesianGPLVM(Y, Q, X=X, X_variance=S, num_inducing=num_inducing, Z=Z, kernel=kernel)
m.data_colors = c
m.data_t = t
m['rbf_lengthscale'] = 1. # X.var(0).max() / X.var(0)
m['noise_variance'] = Y.var() / 100.
m['bias_variance'] = 0.05
if optimize:
m.optimize('scg', messages=1)
return m
