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work on likeluhoods and likelihoods tests

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James Hensman 2014-02-27 15:37:31 +00:00
parent 5cf792504a
commit 2876e5a07a
11 changed files with 186 additions and 222 deletions
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1
GPy/examples/__init__.py
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@ -6,3 +6,4 @@ import regression
import dimensionality_reduction
import tutorials
import stochastic
import non_gaussian

40
GPy/examples/non_gaussian.py
View file

@ -36,28 +36,28 @@ def student_t_approx(optimize=True, plot=True):
edited_real_sd = initial_var_guess
# Kernel object
kernel1 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel2 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel3 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel4 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel1 = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
kernel2 = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
kernel3 = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
kernel4 = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
#Gaussian GP model on clean data
#m1 = GPy.models.GPRegression(X, Y.copy(), kernel=kernel1)
## optimize
#m1['white'].constrain_fixed(1e-5)
#m1.randomize()
m1 = GPy.models.GPRegression(X, Y.copy(), kernel=kernel1)
# optimize
m1['.*white'].constrain_fixed(1e-5)
m1.randomize()
##Gaussian GP model on corrupt data
#m2 = GPy.models.GPRegression(X, Yc.copy(), kernel=kernel2)
#m1['white'].constrain_fixed(1e-5)
#m2.randomize()
m2 = GPy.models.GPRegression(X, Yc.copy(), kernel=kernel2)
m1['.*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
@ -69,8 +69,8 @@ def student_t_approx(optimize=True, plot=True):
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()
if optimize:
@ -153,7 +153,7 @@ def boston_example(optimize=True, plot=True):
#Gaussian GP
print "Gauss GP"
mgp = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelgp.copy())
mgp.constrain_fixed('white', 1e-5)
mgp.constrain_fixed('.*white', 1e-5)
mgp['rbf_len'] = rbf_len
mgp['noise'] = noise
print mgp
@ -171,7 +171,7 @@ def boston_example(optimize=True, plot=True):
g_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), g_distribution)
mg = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu.copy(), likelihood=g_likelihood)
mg.constrain_positive('noise_variance')
mg.constrain_fixed('white', 1e-5)
mg.constrain_fixed('.*white', 1e-5)
mg['rbf_len'] = rbf_len
mg['noise'] = noise
print mg
@ -189,10 +189,10 @@ def boston_example(optimize=True, plot=True):
t_distribution = GPy.likelihoods.noise_model_constructors.student_t(deg_free=df, sigma2=noise)
stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution)
mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu.copy(), likelihood=stu_t_likelihood)
mstu_t.constrain_fixed('white', 1e-5)
mstu_t.constrain_bounded('t_noise', 0.0001, 1000)
mstu_t.constrain_fixed('.*white', 1e-5)
mstu_t.constrain_bounded('.*t_noise', 0.0001, 1000)
mstu_t['rbf_len'] = rbf_len
mstu_t['t_noise'] = noise
mstu_t['.*t_noise'] = noise
print mstu_t
if optimize:
mstu_t.optimize(optimizer=optimizer, messages=messages)