apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-24 14:15:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Log-likelihood,predictions and plotting are working.

Browse source
This commit is contained in:
Ricardo 2013-01-29 23:54:02 +00:00
parent bb1e0021d7
commit d1a0883c12
4 changed files with 64 additions and 56 deletions
Download patch file Download diff file Expand all files Collapse all files

19
GPy/examples/sparse_ep_fix.py
View file

@ -31,18 +31,17 @@ noise = GPy.kern.white(1)
kernel = rbf + noise
# create simple GP model
#m1 = GPy.models.sparse_GP(X, Y, kernel, M=M)
m1 = GPy.models.sparse_GP(X,Y=None, kernel=kernel, M=M,likelihood= likelihood)
m = GPy.models.sparse_GP(X,Y=None, kernel=kernel, M=M,likelihood= likelihood)
#m = GPy.models.sparse_GP(X, Y, kernel, M=M)
print m1.checkgrad()
# contrain all parameters to be positive
m1.constrain_positive('(variance|lengthscale|precision)')
#m1.constrain_positive('(variance|lengthscale)')
#m1.constrain_fixed('prec',10.)
m.ensure_default_constraints()
if not isinstance(m.likelihood,GPy.inference.likelihoods.gaussian):
m.approximate_likelihood()
print m.checkgrad()
#check gradient FIXME unit test please
# optimize and plot
m1.optimize('tnc', messages = 1)
m1.plot()
# print(m1)
#m.optimize('tnc', messages = 1)
m.plot(samples=3,full_cov=False)
# print(m)