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Still working on rasmussen, link function needs vectorizing I think

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Alan Saul 2013-04-02 20:00:31 +01:00
parent afa5b1f956
commit 0312f319ad
3 changed files with 154 additions and 54 deletions
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58
python/examples/laplace_approximations.py
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@ -16,6 +16,9 @@ def student_t_approx():
Y = np.sin(X) + np.random.randn(*X.shape)*real_var
Yc = Y.copy()
X_full = np.linspace(0.0, 10.0, 500)[:, None]
Y_full = np.sin(X_full)
#Y = Y/Y.max()
Yc[10] += 100
@ -25,7 +28,7 @@ def student_t_approx():
#Yc = Yc/Yc.max()
#Add student t random noise to datapoints
deg_free = 20 #100000.5
deg_free = 10
real_sd = np.sqrt(real_var)
#t_rv = t(deg_free, loc=0, scale=real_var)
#noise = t_rvrvs(size=Y.shape)
@ -47,6 +50,8 @@ def student_t_approx():
kernel2 = kernel1.copy()
kernel3 = kernel1.copy()
kernel4 = kernel1.copy()
kernel5 = kernel1.copy()
kernel6 = kernel1.copy()
print "Clean Gaussian"
#A GP should completely break down due to the points as they get a lot of weight
@ -58,6 +63,7 @@ def student_t_approx():
# plot
plt.subplot(211)
m.plot()
plt.plot(X_full, Y_full)
print m
#Corrupt
@ -67,40 +73,64 @@ def student_t_approx():
m.optimize()
plt.subplot(212)
m.plot()
plt.plot(X_full, Y_full)
print m
plt.figure(2)
plt.suptitle('Student-t likelihood')
edited_real_sd = real_sd
# Likelihood object
print "Clean student t, ncg"
t_distribution = student_t(deg_free, sigma=edited_real_sd)
stu_t_likelihood = Laplace(Y, t_distribution)
print "Clean student t"
stu_t_likelihood = Laplace(Y, t_distribution, rasm=False)
m = GPy.models.GP(X, stu_t_likelihood, kernel3)
m.ensure_default_constraints()
m.update_likelihood_approximation()
# optimize
m.optimize()
print(m)
# plot
plt.subplot(211)
plt.subplot(221)
m.plot()
plt.ylim(-2.5,2.5)
#import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
plt.plot(X_full, Y_full)
plt.ylim(-2.5, 2.5)
print "Corrupt student t"
print "Corrupt student t, ncg"
t_distribution = student_t(deg_free, sigma=edited_real_sd)
corrupt_stu_t_likelihood = Laplace(Yc, t_distribution)
corrupt_stu_t_likelihood = 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()
m.optimize()
print(m)
plt.subplot(223)
m.plot()
plt.plot(X_full, Y_full)
plt.ylim(-2.5, 2.5)
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)
m = GPy.models.GP(X, stu_t_likelihood, kernel6)
m.ensure_default_constraints()
m.update_likelihood_approximation()
m.optimize()
print(m)
plt.subplot(222)
m.plot()
plt.plot(X_full, Y_full)
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)
m = GPy.models.GP(X, corrupt_stu_t_likelihood, kernel4)
m.ensure_default_constraints()
m.update_likelihood_approximation()
m.optimize()
print(m)
plt.subplot(212)
plt.subplot(224)
m.plot()
plt.ylim(-2.5,2.5)
plt.plot(X_full, Y_full)
plt.ylim(-2.5, 2.5)
import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
###with a student t distribution, since it has heavy tails it should work well