mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-06 18:42:39 +02:00
33 lines
827 B
Python
33 lines
827 B
Python
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
|
|
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
|
|
|
import numpy as np
|
|
import pylab as pb
|
|
import GPy
|
|
np.random.seed(123344)
|
|
|
|
N = 10
|
|
M = 5
|
|
Q = 3
|
|
D = 4
|
|
#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 = False) + 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)
|
|
|
|
# pb.figure()
|
|
# m.plot()
|
|
# pb.title('PCA initialisation')
|
|
# pb.figure()
|
|
# m.optimize(messages = 1)
|
|
# m.plot()
|
|
# pb.title('After optimisation')
|
|
m.randomize()
|
|
m.checkgrad(verbose = 1)
|