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Working for regression, still some bugs for EP.

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This commit is contained in:
Ricardo Andrade 2013-01-30 16:00:03 +00:00
parent 29eb61d65e
commit d8eb155622
2 changed files with 50 additions and 36 deletions
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31
GPy/examples/sparse_ep_fix.py
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@ -32,18 +32,29 @@ noise = GPy.kern.white(1)
kernel = rbf + noise
# create simple GP model
m = GPy.models.sparse_GP(X,Y=None, kernel=kernel, M=M,likelihood= likelihood)
#m = GPy.models.sparse_GP(X, Y, kernel, M=M)
#m = GPy.models.sparse_GP(X,Y=None, kernel=kernel, M=M,likelihood= likelihood)
# contrain all parameters to be positive
#m.constrain_fixed('prec',100.)
m = GPy.models.sparse_GP(X, Y, kernel, M=M)
m.ensure_default_constraints()
if not isinstance(m.likelihood,GPy.inference.likelihoods.gaussian):
m.approximate_likelihood()
#if not isinstance(m.likelihood,GPy.inference.likelihoods.gaussian):
# m.approximate_likelihood()
print m.checkgrad()
#check gradient FIXME unit test please
# optimize and plot
#m.optimize('tnc', messages = 1)
m.EM()
m.plot(samples=3,full_cov=False)
# print(m)
m.optimize('tnc', messages = 1)
m.plot(samples=3)
print m
n = GPy.models.sparse_GP(X,Y=None, kernel=kernel, M=M,likelihood= likelihood)
n.ensure_default_constraints()
if not isinstance(n.likelihood,GPy.inference.likelihoods.gaussian):
n.approximate_likelihood()
print n.checkgrad()
pb.figure()
n.plot()
"""
m = GPy.models.sparse_GP_regression(X, Y, kernel, M=M)
m.ensure_default_constraints()
print m.checkgrad()
"""