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Should be working now, needed to change relative path names

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Alan Saul 2013-04-15 15:44:29 +01:00
parent 64b95ab1d6
commit 589aeda88c
3 changed files with 14 additions and 20 deletions
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3
GPy/examples/classification.py
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@ -17,8 +17,7 @@ def crescent_data(seed=default_seed): #FIXME
:param seed : seed value for data generation.
:type seed: int
:param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
:type inducing: int
"""
:type inducing: int """
data = GPy.util.datasets.crescent_data(seed=seed)

29
GPy/examples/laplace_approximations.py
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@ -1,10 +1,6 @@
import GPy
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import t, norm
from coxGP.python.likelihoods.Laplace import Laplace
from coxGP.python.likelihoods.likelihood_function import student_t
def timing():
real_var = 0.1
@ -28,15 +24,14 @@ def timing():
edited_real_sd = real_sd
kernel1 = GPy.kern.rbf(X.shape[1])
t_distribution = student_t(deg_free, sigma=edited_real_sd)
corrupt_stu_t_likelihood = Laplace(Yc.copy(), t_distribution, rasm=True)
t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma=edited_real_sd)
corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution, rasm=True)
m = GPy.models.GP(X, corrupt_stu_t_likelihood, kernel1)
m.ensure_default_constraints()
m.update_likelihood_approximation()
m.optimize()
the_is[a] = m.likelihood.i
#import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
print the_is
print np.mean(the_is)
@ -116,8 +111,8 @@ def student_t_approx():
edited_real_sd = real_sd
print "Clean student t, rasm"
t_distribution = student_t(deg_free, sigma=edited_real_sd)
stu_t_likelihood = Laplace(Y.copy(), t_distribution, rasm=True)
t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma=edited_real_sd)
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, rasm=True)
m = GPy.models.GP(X, stu_t_likelihood, kernel6)
m.ensure_default_constraints()
m.update_likelihood_approximation()
@ -129,8 +124,8 @@ def student_t_approx():
plt.ylim(-2.5, 2.5)
print "Corrupt student t, rasm"
t_distribution = student_t(deg_free, sigma=edited_real_sd)
corrupt_stu_t_likelihood = Laplace(Yc.copy(), t_distribution, rasm=True)
t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma=edited_real_sd)
corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution, rasm=True)
m = GPy.models.GP(X, corrupt_stu_t_likelihood, kernel4)
m.ensure_default_constraints()
m.update_likelihood_approximation()
@ -142,8 +137,8 @@ def student_t_approx():
plt.ylim(-2.5, 2.5)
print "Clean student t, ncg"
t_distribution = student_t(deg_free, sigma=edited_real_sd)
stu_t_likelihood = Laplace(Y, t_distribution, rasm=False)
t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma=edited_real_sd)
stu_t_likelihood = GPy.likelihoods.Laplace(Y, t_distribution, rasm=False)
m = GPy.models.GP(X, stu_t_likelihood, kernel3)
m.ensure_default_constraints()
m.update_likelihood_approximation()
@ -155,8 +150,8 @@ def student_t_approx():
plt.ylim(-2.5, 2.5)
print "Corrupt student t, ncg"
t_distribution = student_t(deg_free, sigma=edited_real_sd)
corrupt_stu_t_likelihood = Laplace(Yc.copy(), t_distribution, rasm=False)
t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma=edited_real_sd)
corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution, rasm=False)
m = GPy.models.GP(X, corrupt_stu_t_likelihood, kernel5)
m.ensure_default_constraints()
m.update_likelihood_approximation()
@ -169,8 +164,8 @@ def student_t_approx():
###with a student t distribution, since it has heavy tails it should work well
###likelihood_function = student_t(deg_free, sigma=real_var)
###lap = Laplace(Y, likelihood_function)
###likelihood_functions = student_t(deg_free, sigma=real_var)
###lap = Laplace(Y, likelihood_functions)
###cov = kernel.K(X)
###lap.fit_full(cov)

2
GPy/likelihoods/__init__.py
View file

@ -1,4 +1,4 @@
from EP import EP
from Gaussian import Gaussian
# TODO: from Laplace import Laplace
from Laplace import Laplace
import likelihood_functions as functions