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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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12
GPy/likelihoods/poisson.py
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@ -64,8 +64,7 @@ class Poisson(Likelihood):
:rtype: float
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
return np.sum(-link_f + y*np.log(link_f) - special.gammaln(y+1))
return -link_f + y*np.log(link_f) - special.gammaln(y+1)
def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
"""
@ -83,7 +82,6 @@ class Poisson(Likelihood):
:rtype: Nx1 array
"""
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
return y/link_f - 1
def d2logpdf_dlink2(self, link_f, y, Y_metadata=None):
@ -107,12 +105,7 @@ class Poisson(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 = -y/(link_f**2)
return hess
#d2_df = self.gp_link.d2transf_df2(gp)
#transf = self.gp_link.transf(gp)
#return obs * ((self.gp_link.dtransf_df(gp)/transf)**2 - d2_df/transf) + d2_df
return -y/(link_f**2)
def d3logpdf_dlink3(self, link_f, y, Y_metadata=None):
"""
@ -129,7 +122,6 @@ class Poisson(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 = 2*y/(link_f)**3
return d3lik_dlink3