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Tidied up grad checking

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Alan Saul 2013-09-11 15:27:14 +01:00
parent cf9ea23aef
commit 42f8180c4e
4 changed files with 69 additions and 44 deletions
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20
GPy/examples/laplace_approximations.py
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@ -27,7 +27,7 @@ def timing():
t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution, opt='rasm')
m = GPy.models.GPRegression(X, corrupt_stu_t_likelihood, kernel1)
m = GPy.models.GPRegression(X, Yc.copy(), kernel1, likelihood=corrupt_stu_t_likelihood)
m.ensure_default_constraints()
m.update_likelihood_approximation()
m.optimize()
@ -56,7 +56,7 @@ def v_fail_test():
print "Clean student t, rasm"
t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
m = GPy.models.GPRegression(X, stu_t_likelihood, kernel1)
m = GPy.models.GPRegression(X, Y.copy(), kernel1, likelihood=stu_t_likelihood)
m.constrain_positive('')
vs = 25
noises = 30
@ -103,7 +103,7 @@ def student_t_obj_plane():
kernelst = kernelgp.copy()
t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=(real_std**2))
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
m = GPy.models.GPRegression(X, stu_t_likelihood, kernelst)
m = GPy.models.GPRegression(X, Y, kernelst, likelihood=stu_t_likelihood)
m.ensure_default_constraints()
m.constrain_fixed('t_no', real_std**2)
vs = 10
@ -156,7 +156,7 @@ def student_t_f_check():
#kernelst += GPy.kern.bias(X.shape[1])
t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=0.05)
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
m = GPy.models.GPRegression(X, stu_t_likelihood, kernelst)
m = GPy.models.GPRegression(X, Y.copy(), kernelst, likelihood=stu_t_likelihood)
#m['rbf_v'] = mgp._get_params()[0]
#m['rbf_l'] = mgp._get_params()[1] + 1
m.ensure_default_constraints()
@ -211,7 +211,7 @@ def student_t_fix_optimise_check():
plt.figure(1)
plt.suptitle('Student likelihood')
m = GPy.models.GPRegression(X, stu_t_likelihood, kernelst)
m = GPy.models.GPRegression(X, Y, kernelst, likelihood=stu_t_likelihood)
m.constrain_fixed('rbf_var', mgp._get_params()[0])
m.constrain_fixed('rbf_len', mgp._get_params()[1])
m.constrain_positive('t_noise')
@ -352,7 +352,7 @@ def debug_student_t_noise_approx():
t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
m = GPy.models.GPRegression(X, stu_t_likelihood, kernel6)
m = GPy.models.GPRegression(X, Y, kernel6, likelihood=stu_t_likelihood)
#m['rbf_len'] = 1.5
#m.constrain_fixed('rbf_v', 1.0898)
#m.constrain_fixed('rbf_l', 0.2651)
@ -482,7 +482,7 @@ def student_t_approx():
print "Clean student t, rasm"
t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
m = GPy.models.GPRegression(X, Y.copy(), kernel6, stu_t_likelihood)
m = GPy.models.GPRegression(X, Y.copy(), kernel6, likelihood=stu_t_likelihood)
m.ensure_default_constraints()
m.constrain_positive('t_noise')
m.randomize()
@ -498,7 +498,7 @@ def student_t_approx():
print "Corrupt student t, rasm"
t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution, opt='rasm')
m = GPy.models.GPRegression(X, Yc.copy(), kernel4, corrupt_stu_t_likelihood)
m = GPy.models.GPRegression(X, Yc.copy(), kernel4, likelihood=corrupt_stu_t_likelihood)
m.ensure_default_constraints()
m.constrain_positive('t_noise')
m.randomize()
@ -516,7 +516,7 @@ def student_t_approx():
#print "Clean student t, ncg"
#t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
#stu_t_likelihood = GPy.likelihoods.Laplace(Y, t_distribution, opt='ncg')
#m = GPy.models.GPRegression(X, stu_t_likelihood, kernel3)
#m = GPy.models.GPRegression(X, Y, kernel3, likelihood=stu_t_likelihood)
#m.ensure_default_constraints()
#m.update_likelihood_approximation()
#m.optimize()
@ -530,7 +530,7 @@ def student_t_approx():
#print "Corrupt student t, ncg"
#t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
#corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution, opt='ncg')
#m = GPy.models.GPRegression(X, corrupt_stu_t_likelihood, kernel5)
#m = GPy.models.GPRegression(X, Y, kernel5, likelihood=corrupt_stu_t_likelihood)
#m.ensure_default_constraints()
#m.update_likelihood_approximation()
#m.optimize()