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BGPLVM working with rbf+white

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Nicolo Fusi 2013-01-29 14:02:41 +00:00
parent 8b6e244cf1
commit 936d08723e
5 changed files with 105 additions and 32 deletions
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30
GPy/examples/oil_flow_demo.py
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@ -20,26 +20,28 @@ def plot_oil(X, theta, labels, label):
plt.plot(flow_type[:,0], flow_type[:,1], 'bx')
plt.title(label)
data = pickle.load(open('../util/datasets/oil_flow_3classes.pickle', 'r'))
data = pickle.load(open('../../../GPy_assembla/datasets/oil_flow_3classes.pickle', 'r'))
Y = data['DataTrn']
N, D = Y.shape
selected = np.random.permutation(N)[:200]
selected = np.random.permutation(N)#[:200]
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 = 2
m1 = GPy.models.sparse_GPLVM(Y, Q, M = 15)
m1.constrain_positive('(rbf|bias|noise)')
m1.constrain_bounded('white', 1e-6, 1.0)
Q = 7
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)
plot_oil(m1.X, np.array([1,1]), labels, 'PCA initialization')
plot_oil(m.X, np.array([1,1]), labels, 'PCA initialization')
# m.optimize(messages = True)
m1.optimize('bfgs', messages = True)
plot_oil(m1.X, np.array([1,1]), labels, 'sparse GPLVM')
m.optimize('tnc', messages = True)
plot_oil(m.X, m.kern.parts[0].lengthscales, labels, 'B-GPLVM')
# pb.figure()
# m.plot()
# pb.title('PCA initialisation')
@ -47,7 +49,7 @@ plot_oil(m1.X, np.array([1,1]), labels, 'sparse GPLVM')
# m.optimize(messages = 1)
# m.plot()
# pb.title('After optimisation')
m = GPy.models.GPLVM(Y, Q)
m.constrain_positive('(white|rbf|bias|noise)')
m.optimize()
plot_oil(m.X, np.array([1,1]), labels, 'GPLVM')
# m = GPy.models.GPLVM(Y, Q)
# m.constrain_positive('(white|rbf|bias|noise)')
# m.optimize()
# plot_oil(m.X, np.array([1,1]), labels, 'GPLVM')