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Tidying up and fixed objective being vector

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This commit is contained in:
Alan Saul 2013-10-28 15:42:25 +00:00
parent 336f8e11c4
commit fc59ef4baf
3 changed files with 12 additions and 9 deletions
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8
GPy/likelihoods/laplace.py
View file

@ -340,8 +340,8 @@ class Laplace(likelihood):
Ki_f = old_Ki_f + step_size*dKi_f
f = np.dot(K, Ki_f)
# This is nasty, need to set something within an optimization though
self.Ki_f = Ki_f.copy()
self.f = f.copy()
self.tmp_Ki_f = Ki_f.copy()
self.tmp_f = f.copy()
return -obj(Ki_f, f)
i_o = partial_func(inner_obj, old_Ki_f=old_Ki_f, dKi_f=dKi_f, K=K)
@ -349,8 +349,8 @@ class Laplace(likelihood):
#The tolerance and maxiter matter for speed! Seems to be best to keep them low and make more full
#steps than get this exact then make a step, if B was bigger it might be the other way around though
new_obj = sp.optimize.minimize_scalar(i_o, method='brent', tol=1e-4, options={'maxiter':5}).fun
f = self.f.copy()
Ki_f = self.Ki_f.copy()
f = self.tmp_f.copy()
Ki_f = self.tmp_Ki_f.copy()
#Optimize without linesearch
#f_old = f.copy()

7
GPy/likelihoods/noise_models/exponential_noise.py
View file

@ -40,7 +40,8 @@ class Exponential(NoiseDistribution):
:rtype: float
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
return np.exp(np.sum(np.log(link_f*np.exp(-y*link_f))))
log_objective = link_f*np.exp(-y*link_f)
return np.exp(np.sum(np.log(log_objective)))
#return np.exp(np.sum(-y/link_f - np.log(link_f) ))
def logpdf_link(self, link_f, y, extra_data=None):
@ -60,9 +61,9 @@ class Exponential(NoiseDistribution):
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
logpdf_link = np.sum(np.log(link_f) - y*link_f)
log_objective = np.log(link_f) - y*link_f
#logpdf_link = np.sum(-np.log(link_f) - y/link_f)
return logpdf_link
return np.sum(log_objective)
def dlogpdf_dlink(self, link_f, y, extra_data=None):
"""

6
GPy/likelihoods/noise_models/gamma_noise.py
View file

@ -44,7 +44,8 @@ class Gamma(NoiseDistribution):
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
#return stats.gamma.pdf(obs,a = self.gp_link.transf(gp)/self.variance,scale=self.variance)
alpha = link_f*self.beta
return (y**(alpha - 1.) * np.exp(-self.beta*y) * self.beta**alpha)/ special.gamma(alpha)
objective = (y**(alpha - 1.) * np.exp(-self.beta*y) * self.beta**alpha)/ special.gamma(alpha)
return np.exp(np.sum(np.log(objective)))
def logpdf_link(self, link_f, y, extra_data=None):
"""
@ -67,7 +68,8 @@ class Gamma(NoiseDistribution):
#alpha = self.gp_link.transf(gp)*self.beta
#return (1. - alpha)*np.log(obs) + self.beta*obs - alpha * np.log(self.beta) + np.log(special.gamma(alpha))
alpha = link_f*self.beta
return alpha*np.log(self.beta) - np.log(special.gamma(alpha)) + (alpha - 1)*np.log(y) - self.beta*y
log_objective = alpha*np.log(self.beta) - np.log(special.gamma(alpha)) + (alpha - 1)*np.log(y) - self.beta*y
return np.sum(log_objective)
def dlogpdf_dlink(self, link_f, y, extra_data=None):
"""