def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=25, Q=4, sigma=.2):
import GPy
from pods.datasets import swiss_roll_generated
from GPy.models import BayesianGPLVM
data = swiss_roll_generated(num_samples=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:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # @UnusedImport
fig = plt.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("BGPLVM init")
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
if optimize:
m.optimize('bfgs', messages=verbose, max_iters=2e3)
if plot:
fig = plt.figure('fitted')
ax = fig.add_subplot(111)
s = m.input_sensitivity().argsort()[::-1][:2]
ax.scatter(*m.X.mean.T[s], c=c)
return m
def swiss_roll(optimize=True,
verbose=1,
plot=True,
N=1000,
num_inducing=25,
Q=4,
sigma=.2):
import GPy
from pods.datasets import swiss_roll_generated
from GPy.models import BayesianGPLVM
data = swiss_roll_generated(num_samples=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:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # @UnusedImport
fig = plt.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("BGPLVM init")
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
if optimize:
m.optimize('bfgs', messages=verbose, max_iters=2e3)
if plot:
fig = plt.figure('fitted')
ax = fig.add_subplot(111)
s = m.input_sensitivity().argsort()[::-1][:2]
ax.scatter(*m.X.mean.T[s], c=c)
return m