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trying to fix bugs in kerns

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Nicolo Fusi 2013-02-12 17:45:44 +00:00
parent 7fbc6935d9
commit 03c1f77c08
6 changed files with 58 additions and 37 deletions
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10
GPy/examples/BGPLVM_demo.py
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@ -7,17 +7,17 @@ import GPy
np.random.seed(123344)
N = 10
M = 5
Q = 3
D = 4
M = 3
Q = 4
D = 5
#generate GPLVM-like data
X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
# k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
k = GPy.kern.linear(Q, ARD = True) + GPy.kern.white(Q, 0.00001)
k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q)
# k = GPy.kern.linear(Q, ARD = True) + GPy.kern.white(Q, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
m.constrain_positive('(rbf|bias|noise|white|S)')
# m.constrain_fixed('S', 1)

30
GPy/examples/oil_flow_demo.py
View file

@ -7,7 +7,7 @@ import numpy as np
import pylab as pb
import GPy
import pylab as plt
np.random.seed(1)
np.random.seed(3)
def plot_oil(X, theta, labels, label):
plt.figure()
@ -24,25 +24,27 @@ data = pickle.load(open('../../../GPy_assembla/datasets/oil_flow_3classes.pickle
Y = data['DataTrn']
N, D = Y.shape
selected = np.random.permutation(N)#[:200]
selected = np.random.permutation(N)[:350]
labels = data['DataTrnLbls'][selected]
Y = Y[selected]
N, D = Y.shape
Y -= Y.mean(axis=0)
#Y /= Y.std(axis=0)
# Y /= Y.std(axis=0)
Q = 10
k = GPy.kern.rbf_ARD(Q) + GPy.kern.white(Q)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M = 12)
m.constrain_positive('(rbf|bias|S|white|noise)')
# m.constrain_bounded('white', 1e-6, 100.0)
# m.constrain_bounded('noise', 1e-4, 1000.0)
Q = 5
k = GPy.kern.linear(Q, ARD = False) + GPy.kern.white(Q)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M = 20)
m.constrain_positive('(rbf|bias|S|linear|white|noise)')
plot_oil(m.X, np.array([1,1]), labels, 'PCA initialization')
# m.optimize(messages = True)
m.optimize('tnc', messages = True)
plot_oil(m.X, m.kern.parts[0].lengthscales, labels, 'B-GPLVM')
# pb.figure()
# m.unconstrain('noise')
# m.constrain_fixed('noise_precision', 50.0)
# m.unconstrain('white')
# m.constrain_bounded('white', 1e-6, 10.0)
# plot_oil(m.X, np.array([1,1]), labels, 'PCA initialization')
m.optimize(messages = True)
# m.optimize('tnc', messages = True)
plot_oil(m.X, m.kern.parts[0].lengthscale, labels, 'B-GPLVM')
# # pb.figure()
# m.plot()
# pb.title('PCA initialisation')
# pb.figure()