apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-06-11 15:15:15 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Playing trying to find what makes it want to go so low

Browse source
This commit is contained in:
Alan Saul 2013-07-02 11:14:48 +01:00
parent a7169ab1ab
commit ab6a3a571e
4 changed files with 26 additions and 19 deletions
Download patch file Download diff file Expand all files Collapse all files

2
GPy/core/model.py
View file

@ -246,7 +246,7 @@ class model(parameterised):
obj_grads = -LL_gradients - prior_gradients
print self
#self.checkgrad(verbose=1)
import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
#import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
return obj_f, obj_grads
def optimize(self, optimizer=None, start=None, **kwargs):

21
GPy/examples/laplace_approximations.py
View file

@ -88,9 +88,12 @@ def debug_student_t_noise_approx():
plot = False
real_var = 0.1
#Start a function, any function
X = np.linspace(0.0, 10.0, 50)[:, None]
#X = np.linspace(0.0, 10.0, 50)[:, None]
X = np.random.rand(100)[:, None]
#X = np.random.rand(100)[:, None]
#X = np.array([0.5, 1])[:, None]
Y = np.sin(X) + np.random.randn(*X.shape)*real_var
Y = np.sin(X*2*np.pi) + np.random.randn(*X.shape)*real_var
#Y = X + np.random.randn(*X.shape)*real_var
#ty = np.array([1., 9.97733584, 4.17841363])[:, None]
#Y = ty
@ -112,7 +115,8 @@ def debug_student_t_noise_approx():
plt.close('all')
# Kernel object
kernel1 = 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])
#kernel1 = GPy.kern.linear(X.shape[1]) + GPy.kern.white(X.shape[1])
kernel2 = kernel1.copy()
kernel3 = kernel1.copy()
kernel4 = kernel1.copy()
@ -136,7 +140,7 @@ def debug_student_t_noise_approx():
#print m
real_stu_t_std = np.sqrt(real_var*((deg_free - 2)/float(deg_free)))
edited_real_sd = real_stu_t_std#initial_var_guess #real_sd
edited_real_sd = real_stu_t_std + 1#initial_var_guess #real_sd
#edited_real_sd = real_sd
print "Clean student t, rasm"
@ -149,13 +153,16 @@ def debug_student_t_noise_approx():
#m.constrain_fixed('rbf_l', 1.8651)
#m.constrain_fixed('t_noise_std', edited_real_sd)
#m.constrain_positive('rbf')
m.constrain_positive('t_noise_std')
#m.constrain_positive('t_noise_std')
#m.constrain_positive('')
m.ensure_default_constraints()
m.constrain_bounded('t_noi', 0.001, 10)
#m.constrain_bounded('t_noi', 0.001, 10)
#m.constrain_fixed('t_noi', real_stu_t_std)
m.constrain_fixed('white', 0.01)
#m.constrain_fixed('t_no', 0.01)
#m['rbf_var'] = 0.20446332
#m['rbf_leng'] = 0.85776241
#m['t_noise'] = 0.667083294421005
m.ensure_default_constraints()
m.update_likelihood_approximation()
#m.optimize(messages=True)
print(m)

18
GPy/likelihoods/Laplace.py
View file

@ -156,15 +156,15 @@ class Laplace(likelihood):
Y_tilde = Wi*self.Ki_f + self.f_hat
self.Wi_K_i = self.W_12*self.Bi*self.W_12.T #same as rasms R
ln_det_K_Wi__Bi = self.ln_I_KW_det + pddet(self.Sigma_tilde + self.K)
l = self.likelihood_function.link_function(self.data, self.f_hat, extra_data=self.extra_data)
self.ln_det_K_Wi__Bi = self.ln_I_KW_det + pddet(self.Sigma_tilde + self.K)
self.lik = self.likelihood_function.link_function(self.data, self.f_hat, extra_data=self.extra_data)
y_Wi_Ki_i_y = mdot(Y_tilde.T, self.Wi_K_i, Y_tilde)
Z_tilde = (+ self.NORMAL_CONST
+ l
+ 0.5*ln_det_K_Wi__Bi
self.y_Wi_Ki_i_y = mdot(Y_tilde.T, self.Wi_K_i, Y_tilde)
Z_tilde = (#+ self.NORMAL_CONST
+ self.lik
+ 0.5*self.ln_det_K_Wi__Bi
- 0.5*self.f_Ki_f
+ 0.5*y_Wi_Ki_i_y
+ 0.5*self.y_Wi_Ki_i_y
)
#print "Ztilde: {}".format(Z_tilde)
@ -198,7 +198,7 @@ class Laplace(likelihood):
self.W = -self.likelihood_function.d2lik_d2f(self.data, self.f_hat, extra_data=self.extra_data)
if not self.likelihood_function.log_concave:
self.W[self.W < 0] = 1e-6 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
self.W[self.W < 0] = 1e-10 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
#If the likelihood is non-log-concave. We wan't to say that there is a negative variance
#To cause the posterior to become less certain than the prior and likelihood,
#This is a property only held by non-log-concave likelihoods
@ -311,7 +311,7 @@ class Laplace(likelihood):
while difference > epsilon and i < MAX_ITER and rs < MAX_RESTART:
W = -self.likelihood_function.d2lik_d2f(self.data, f, extra_data=self.extra_data)
if not self.likelihood_function.log_concave:
W[W < 0] = 1e-6 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
W[W < 0] = 1e-10 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
# If the likelihood is non-log-concave. We wan't to say that there is a negative variance
# To cause the posterior to become less certain than the prior and likelihood,
# This is a property only held by non-log-concave likelihoods

4
GPy/likelihoods/likelihood_functions.py
View file

@ -196,8 +196,8 @@ class student_t(likelihood_function):
objective = (+ gammaln((self.v + 1) * 0.5)
- gammaln(self.v * 0.5)
- 0.5*np.log((self.sigma**2) * self.v * np.pi)
- (self.v + 1) * 0.5 * np.log(1 + (((e / self.sigma)**2) / self.v))
#- (self.v + 1) * 0.5 * np.log(1 + (e**2)/(self.v*(self.sigma**2)))
#- (self.v + 1) * 0.5 * np.log(1 + (((e / self.sigma)**2) / self.v))
- (self.v + 1) * 0.5 * np.log(1 + (e**2)/(self.v*(self.sigma**2)))
)
return np.sum(objective)