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Fixed merge conflicts, M now num_inducing

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Alan Saul 2013-06-05 15:41:48 +01:00
commit 39c242a4d5
20 changed files with 537 additions and 232 deletions
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5
GPy/examples/regression.py
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@ -159,13 +159,13 @@ def coregionalisation_sparse(optim_iters=100):
k = k1.prod(k2,tensor=True) + GPy.kern.white(2,0.001)
m = GPy.models.SparseGPRegression(X,Y,kernel=k,Z=Z)
m.scale_factor = 10000.
m.constrain_fixed('.*rbf_var',1.)
#m.constrain_positive('kappa')
m.constrain_fixed('iip')
m.constrain_bounded('noise_variance',1e-3,1e-1)
m.ensure_default_constraints()
m.optimize_restarts(5, robust=True, messages=1, max_f_eval=optim_iters)
#plotting:
pb.figure()
Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1))))
Xtest2 = np.hstack((np.linspace(0,9,100)[:,None],np.ones((100,1))))
@ -300,7 +300,6 @@ def sparse_GP_regression_2D(N = 400, num_inducing = 50, optim_iters=100):
m.checkgrad()
# optimize and plot
pb.figure()
m.optimize('tnc', messages = 1, max_f_eval=optim_iters)
m.plot()
print(m)