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Changed kernels in tests (lots still failing, but now mostly for good reason rather than silly naming problems)

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This commit is contained in:
Alan Saul 2014-03-04 11:28:29 +00:00
parent ca40b9803a
commit 9888d768b5
7 changed files with 92 additions and 87 deletions
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29
GPy/examples/non_gaussian.py
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@ -30,7 +30,7 @@ def student_t_approx(optimize=True, plot=True):
#Yc = Yc/Yc.max()
#Add student t random noise to datapoints
deg_free = 5
deg_free = 1
print "Real noise: ", real_std
initial_var_guess = 0.5
edited_real_sd = initial_var_guess
@ -44,34 +44,39 @@ def student_t_approx(optimize=True, plot=True):
#Gaussian GP model on clean data
m1 = GPy.models.GPRegression(X, Y.copy(), kernel=kernel1)
# optimize
m1['white'].constrain_fixed(1e-5)
m1['.*white'].constrain_fixed(1e-5)
m1.randomize()
#Gaussian GP model on corrupt data
m2 = GPy.models.GPRegression(X, Yc.copy(), kernel=kernel2)
m2['white'].constrain_fixed(1e-5)
m2['.*white'].constrain_fixed(1e-5)
m2.randomize()
#Student t GP model on clean data
t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
laplace_inf = GPy.inference.latent_function_inference.Laplace()
m3 = GPy.core.GP(X, Y.copy(), kernel3, likelihood=t_distribution, inference_method=laplace_inf)
m3['t_noise'].constrain_bounded(1e-6, 10.)
m3['white'].constrain_fixed(1e-5)
m3['.*t_noise'].constrain_bounded(1e-6, 10.)
m3['.*white'].constrain_fixed(1e-5)
m3.randomize()
debug = True
print m3
if debug:
m3.optimize(messages=1)
return m3
#Student t GP model on corrupt data
t_distribution = GPy.likelihoods.StudentT(deg_free=deg_free, sigma2=edited_real_sd)
laplace_inf = GPy.inference.latent_function_inference.Laplace()
m4 = GPy.core.GP(X, Yc.copy(), kernel4, likelihood=t_distribution, inference_method=laplace_inf)
m4['t_noise'].constrain_bounded(1e-6, 10.)
m4['white'].constrain_fixed(1e-5)
m4['.*t_noise'].constrain_bounded(1e-6, 10.)
m4['.*white'].constrain_fixed(1e-5)
m4.randomize()
print m4
debug=True
if debug:
m4.optimize(messages=1)
import pylab as pb
pb.plot(m4.X, m4.inference_method.f_hat)
pb.plot(m4.X, m4.Y, 'rx')
m4.plot()
print m4
return m4
if optimize:
optimizer='scg'