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Merge from upstream

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Mike Croucher 2015-03-13 14:23:40 +00:00
commit 7930eb646f
16 changed files with 214 additions and 103 deletions
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GPy/likelihoods/gamma.py
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@ -66,12 +66,11 @@ class Gamma(Likelihood):
:rtype: float
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
#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
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)
return log_objective
def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
"""
@ -90,7 +89,6 @@ class Gamma(Likelihood):
:rtype: Nx1 array
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
grad = self.beta*np.log(self.beta*y) - special.psi(self.beta*link_f)*self.beta
#old
#return -self.gp_link.dtransf_df(gp)*self.beta*np.log(obs) + special.psi(self.gp_link.transf(gp)*self.beta) * self.gp_link.dtransf_df(gp)*self.beta
@ -118,7 +116,6 @@ class Gamma(Likelihood):
Will return diagonal of hessian, since every where else it is 0, as the likelihood factorizes over cases
(the distribution for y_i depends only on link(f_i) not on link(f_(j!=i))
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
hess = -special.polygamma(1, self.beta*link_f)*(self.beta**2)
#old
#return -self.gp_link.d2transf_df2(gp)*self.beta*np.log(obs) + special.polygamma(1,self.gp_link.transf(gp)*self.beta)*(self.gp_link.dtransf_df(gp)*self.beta)**2 + special.psi(self.gp_link.transf(gp)*self.beta)*self.gp_link.d2transf_df2(gp)*self.beta
@ -140,6 +137,5 @@ class Gamma(Likelihood):
:returns: third derivative of likelihood evaluated at points f
:rtype: Nx1 array
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
d3lik_dlink3 = -special.polygamma(2, self.beta*link_f)*(self.beta**3)
return d3lik_dlink3