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swiss_roll example added, BGPLVM_oil now working

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Max Zwiessele 2013-05-16 13:47:55 +01:00
parent 61a79c5041
commit 93d517f24e
5 changed files with 137 additions and 173 deletions
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60
GPy/examples/dimensionality_reduction.py
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@ -6,6 +6,7 @@ from matplotlib import pyplot as plt
import GPy
from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
from GPy.util.datasets import swiss_roll_generated
default_seed = np.random.seed(123344)
@ -61,15 +62,18 @@ def GPLVM_oil_100(optimize=True):
m.plot_latent(labels=m.data_labels)
return m
def swiss_roll(optimize=True, N=1000, M=15, Q=4):
def swiss_roll(optimize=True, N=1000, M=15, Q=4, sigma=.2, plot=False):
from GPy.util.datasets import swiss_roll
from GPy.core.transformations import logexp_clipped
data = swiss_roll(N=N)
data = swiss_roll_generated(N=N, sigma=sigma)
Y = data['Y']
Y -= Y.mean(0)
Y /= Y.std(0)
t = data['t']
c = data['colors']
try:
from sklearn.manifold.isomap import Isomap
iso = Isomap().fit(Y)
@ -79,16 +83,33 @@ def swiss_roll(optimize=True, N=1000, M=15, Q=4):
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
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)[:M]
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, 2)
m = Bayesian_GPLVM(Y, Q, X=X, X_variance=S, M=M, Z=Z, kernel=kernel)
m.data_colors = c
m.data_t = t
# m.constrain('variance|length', logexp_clipped())
m['lengthscale'] = X.var(0) / X.var(0).max()
m.constrain('variance|length', logexp_clipped())
m['lengthscale'] = X.var(0).max() / X.var(0)
m['noise'] = Y.var() / 100.
m.ensure_default_constraints()
@ -96,36 +117,33 @@ def swiss_roll(optimize=True, N=1000, M=15, Q=4):
m.optimize('scg', messages=1)
return m
def BGPLVM_oil(optimize=True, N=100, Q=10, M=10, max_f_eval=1e3, plot=False, **k):
def BGPLVM_oil(optimize=True, N=100, Q=5, M=25, max_f_eval=4e3, plot=False, **k):
data = GPy.util.datasets.oil()
from GPy.core.transformations import logexp_clipped
np.random.seed(0)
# create simple GP model
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
Y = data['X'][:N]
Y -= Y.mean(0)
Yn = Y - Y.mean(0)
Yn /= Yn.std(0)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=kernel, M=M, **k)
m = GPy.models.Bayesian_GPLVM(Yn, Q, kernel=kernel, M=M, **k)
m.data_labels = data['Y'][:N].argmax(axis=1)
m.constrain('variance', logexp_clipped())
m.constrain('length', logexp_clipped())
m['lengt'] = 1.
m['noise'] = Y.var() / 100.
# m.constrain('variance', logexp_clipped())
# m.constrain('length', logexp_clipped())
m['lengt'] = m.X.var(0).max() / m.X.var(0)
m['noise'] = Yn.var() / 100.
m.ensure_default_constraints()
# optimize
if optimize:
m.unconstrain('X'); m.constrain_fixed('X')
m.optimize('scg', messages=1, max_f_eval=10)
m.unconstrain('X'); m.constrain('X_var', logexp_clipped())
m.unconstrain('noise'); m.constrain_fixed('noise', Y.var() / 100.)
m.optimize('scg', messages=1, max_f_eval=150)
m.unconstrain('noise')
m.constrain('noise', logexp_clipped())
# m.unconstrain('noise'); m.constrain_fixed('noise')
# m.optimize('scg', messages=1, max_f_eval=200)
# m.unconstrain('noise')
# m.constrain('noise', logexp_clipped())
m.optimize('scg', messages=1, max_f_eval=max_f_eval)
if plot: