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Everything seems to be gradchecking again

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Alan Saul 2013-07-01 10:06:20 +01:00
parent c90b1f0c99
commit 26b3855af5
4 changed files with 17 additions and 13 deletions
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7
GPy/examples/laplace_approximations.py
View file

@ -91,6 +91,8 @@ def debug_student_t_noise_approx():
X = np.linspace(0.0, 10.0, 50)[:, None]
#X = np.array([0.5, 1])[:, None]
Y = np.sin(X) + np.random.randn(*X.shape)*real_var
#ty = np.array([1., 9.97733584, 4.17841363])[:, None]
#Y = ty
X_full = X
Y_full = np.sin(X_full)
@ -98,7 +100,7 @@ def debug_student_t_noise_approx():
Y = Y/Y.max()
#Add student t random noise to datapoints
deg_free = 100
deg_free = 10000
real_sd = np.sqrt(real_var)
print "Real noise std: ", real_sd
@ -151,6 +153,9 @@ def debug_student_t_noise_approx():
#m.constrain_positive('')
m.ensure_default_constraints()
#m.constrain_fixed('t_noi', real_sd)
#m['rbf_var'] = 0.20446332
#m['rbf_leng'] = 0.85776241
#m['t_noise'] = 0.667083294421005
m.update_likelihood_approximation()
#m.optimize(messages=True)
print(m)

18
GPy/likelihoods/Laplace.py
View file

@ -153,7 +153,7 @@ class Laplace(likelihood):
Wi = 1.0/self.W
self.Sigma_tilde = np.diagflat(Wi)
Y_tilde = Wi*(self.Ki_f + self.W*self.f_hat)
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)
@ -199,7 +199,7 @@ 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-5 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
self.W[self.W < 0] = 1e-8 # 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
@ -312,7 +312,7 @@ 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] = 0#1e-6 # 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
@ -329,8 +329,9 @@ class Laplace(likelihood):
full_step_a = b - W_12*solve_L
da = full_step_a - old_a
f_old = self.f.copy()
f_old = f.copy()
f_old = self.f.copy()
def inner_obj(step_size, old_a, da, K):
a = old_a + step_size*da
f = np.dot(K, a)
@ -340,7 +341,6 @@ class Laplace(likelihood):
from functools import partial
i_o = partial(inner_obj, old_a=old_a, da=da, K=self.K)
old_obj = new_obj
new_obj = sp.optimize.brent(i_o, tol=1e-4, maxiter=10)
#update_passed = False
@ -354,10 +354,10 @@ class Laplace(likelihood):
#print "difference: ",difference
#if difference < 0:
##print grad
#print "Objective function rose", np.float(difference)
##print "Objective function rose", np.float(difference)
##If the objective function isn't rising, restart optimization
#step_size *= 0.8
#print "Reducing step-size to {ss:.3} and restarting optimization".format(ss=step_size)
##print "Reducing step-size to {ss:.3} and restarting optimization".format(ss=step_size)
##objective function isn't increasing, try reducing step size
##f = f_old #it's actually faster not to go back to old location and just zigzag across the mode
##old_obj = tmp_old_obj
@ -368,12 +368,12 @@ class Laplace(likelihood):
f = self.f
difference = new_obj - old_obj
difference = np.abs(np.sum(f - f_old)) + abs(difference)
difference = np.abs(np.sum(f - f_old)) #+ abs(difference)
old_a = self.a #a
i += 1
#print "Positive difference obj: ", np.float(difference)
print "Iterations: {}, Step size reductions: {}, Final_difference: {}, step_size: {}".format(i, rs, difference, step_size)
#print "Iterations: {}, Step size reductions: {}, Final_difference: {}, step_size: {}".format(i, rs, difference, step_size)
#self.a = a
self.B, self.B_chol, self.W_12 = B, L, W_12
self.Bi, _, _, B_det = pdinv(self.B)

2
GPy/likelihoods/likelihood_functions.py
View file

@ -274,7 +274,7 @@ class student_t(likelihood_function):
"""
assert y.shape == f.shape
e = y - f
dlik_grad_dsigma = ((-2*self.sigma*self.v*(self.v + 1)*e)
dlik_grad_dsigma = ((-2*self.sigma*self.v*(self.v + 1)*e) #2 might not want to be here?
/ ((self.v*(self.sigma**2) + e**2)**2)
)
return dlik_grad_dsigma

3
GPy/models/GP.py
View file

@ -152,8 +152,7 @@ class GP(model):
else:
dL_dthetaL = self.likelihood._gradients(partial=np.diag(self.dL_dK))
#print "Stacked dL_dthetaK, dL_dthetaL: ", np.hstack((dL_dthetaK, dL_dthetaL))
print "dL_dthetaK is: ", dL_dthetaK
print "dL_dthetaL is: ", dL_dthetaL
print "dL_dthetaK: {} dL_dthetaL: {}".format(dL_dthetaK, dL_dthetaL)
return np.hstack((dL_dthetaK, dL_dthetaL))
#return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))