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Removed unneeded dependency

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This commit is contained in:
Alan Saul 2013-09-09 17:44:08 +01:00
parent c46a1aaa40
commit 5b25273d2b
2 changed files with 13 additions and 13 deletions
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24
GPy/examples/laplace_approximations.py
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@ -25,7 +25,7 @@ def timing():
edited_real_sd = real_sd
kernel1 = GPy.kern.rbf(X.shape[1])
t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma2=edited_real_sd)
t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution, opt='rasm')
m = GPy.models.GP(X, corrupt_stu_t_likelihood, kernel1)
m.ensure_default_constraints()
@ -54,7 +54,7 @@ def v_fail_test():
edited_real_sd = real_sd
print "Clean student t, rasm"
t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma2=edited_real_sd)
t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
m = GPy.models.GP(X, stu_t_likelihood, kernel1)
m.constrain_positive('')
@ -101,7 +101,7 @@ def student_t_obj_plane():
print mgp
kernelst = kernelgp.copy()
t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma2=(real_std**2))
t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=(real_std**2))
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
m = GPy.models.GP(X, stu_t_likelihood, kernelst)
m.ensure_default_constraints()
@ -154,7 +154,7 @@ def student_t_f_check():
kernelst = kernelgp.copy()
#kernelst += GPy.kern.bias(X.shape[1])
t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma2=0.05)
t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=0.05)
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
m = GPy.models.GP(X, stu_t_likelihood, kernelst)
#m['rbf_v'] = mgp._get_params()[0]
@ -206,7 +206,7 @@ def student_t_fix_optimise_check():
kernelst = kernelgp.copy()
real_stu_t_std2 = (real_std**2)*((deg_free - 2)/float(deg_free))
t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma2=real_stu_t_std2)
t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=real_stu_t_std2)
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
plt.figure(1)
@ -349,7 +349,7 @@ def debug_student_t_noise_approx():
#edited_real_sd = real_sd
print "Clean student t, rasm"
t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma2=edited_real_sd)
t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
m = GPy.models.GP(X, stu_t_likelihood, kernel6)
@ -384,7 +384,7 @@ def debug_student_t_noise_approx():
return m
#print "Clean student t, ncg"
#t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma2=edited_real_sd)
#t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
#stu_t_likelihood = GPy.likelihoods.Laplace(Y, t_distribution, opt='ncg')
#m = GPy.models.GP(X, stu_t_likelihood, kernel3)
#m.ensure_default_constraints()
@ -480,7 +480,7 @@ def student_t_approx():
edited_real_sd = real_std #initial_var_guess
print "Clean student t, rasm"
t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma2=edited_real_sd)
t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
m = GPy.models.GP(X, stu_t_likelihood, kernel6)
m.ensure_default_constraints()
@ -496,7 +496,7 @@ def student_t_approx():
plt.title('Student-t rasm clean')
print "Corrupt student t, rasm"
t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma2=edited_real_sd)
t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution, opt='rasm')
m = GPy.models.GP(X, corrupt_stu_t_likelihood, kernel4)
m.ensure_default_constraints()
@ -514,7 +514,7 @@ def student_t_approx():
return m
#print "Clean student t, ncg"
#t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma2=edited_real_sd)
#t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
#stu_t_likelihood = GPy.likelihoods.Laplace(Y, t_distribution, opt='ncg')
#m = GPy.models.GP(X, stu_t_likelihood, kernel3)
#m.ensure_default_constraints()
@ -528,7 +528,7 @@ def student_t_approx():
#plt.title('Student-t ncg clean')
#print "Corrupt student t, ncg"
#t_distribution = GPy.likelihoods.likelihood_functions.student_t(deg_free, sigma2=edited_real_sd)
#t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
#corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution, opt='ncg')
#m = GPy.models.GP(X, corrupt_stu_t_likelihood, kernel5)
#m.ensure_default_constraints()
@ -612,7 +612,7 @@ def gaussian_f_check():
kernelg = kernelgp.copy()
#kernelst += GPy.kern.bias(X.shape[1])
N, D = X.shape
g_distribution = GPy.likelihoods.likelihood_functions.gaussian(variance=0.1, N=N, D=D)
g_distribution = GPy.likelihoods.likelihood_functions.Gaussian(variance=0.1, N=N, D=D)
g_likelihood = GPy.likelihoods.Laplace(Y.copy(), g_distribution, opt='rasm')
m = GPy.models.GP(X, g_likelihood, kernelg)
#m['rbf_v'] = mgp._get_params()[0]

2
GPy/likelihoods/Laplace.py
View file

@ -4,7 +4,7 @@ import GPy
from scipy.linalg import inv, cho_solve, det
from numpy.linalg import cond
from likelihood import likelihood
from ..util.linalg import pdinv, mdot, jitchol, chol_inv, det_ln_diag, pddet
from ..util.linalg import pdinv, mdot, jitchol, chol_inv, pddet
from scipy.linalg.lapack import dtrtrs
import random
#import pylab as plt