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This commit is contained in:
Ricardo 2013-07-15 18:09:26 +01:00
parent 68e37e8684
commit 5b433a3f73
4 changed files with 19 additions and 5 deletions
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7
GPy/core/gp.py
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@ -98,7 +98,12 @@ class GP(GPBase):
Note, we use the chain rule: dL_dtheta = dL_dK * d_K_dtheta Note, we use the chain rule: dL_dtheta = dL_dK * d_K_dtheta
""" """
return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK)))) #return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
if not isinstance(self.likelihood,EP):
tmp = np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
else:
tmp = np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
return tmp
def _raw_predict(self, _Xnew, which_parts='all', full_cov=False,stop=False): def _raw_predict(self, _Xnew, which_parts='all', full_cov=False,stop=False):
""" """

3
GPy/likelihoods/ep.py
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@ -77,7 +77,8 @@ class EP(likelihood):
self.noise_model._set_params(p) self.noise_model._set_params(p)
def _gradients(self,partial): def _gradients(self,partial):
return np.zeros(0) # TODO: the EP likelihood might want to take some parameters... #return np.zeros(0) # TODO: the EP likelihood might want to take some parameters...
return self.noise_model._gradients(partial)
def _compute_GP_variables(self): def _compute_GP_variables(self):
#Variables to be called from GP #Variables to be called from GP

11
GPy/likelihoods/noise_models/gaussian_noise.py
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@ -28,6 +28,10 @@ class Gaussian(NoiseDistribution):
def _set_params(self,p): def _set_params(self,p):
self.variance = p self.variance = p
def _gradients(self,partial):
return np.zeros(1)
#return np.sum(partial)
def _preprocess_values(self,Y): def _preprocess_values(self,Y):
""" """
Check if the values of the observations correspond to the values Check if the values of the observations correspond to the values
@ -57,13 +61,14 @@ class Gaussian(NoiseDistribution):
return 1./(1./self.variance + 1./sigma**2) return 1./(1./self.variance + 1./sigma**2)
def _mass(self,gp,obs): def _mass(self,gp,obs):
return std_norm_pdf( (self.gp_link.transf(gp)-obs)/np.sqrt(self.variance) ) #return std_norm_pdf( (self.gp_link.transf(gp)-obs)/np.sqrt(self.variance) )
return stats.norm.pdf(obs,self.gp_link.transf(gp),np.sqrt(self.variance)) #FIXME
def _nlog_mass(self,gp,obs): def _nlog_mass(self,gp,obs):
return .5*((self.gp_link.transf(gp)-obs)**2/np.sqrt(self.variance) + np.log(2*np.pi*self.variance)) return .5*((self.gp_link.transf(gp)-obs)**2/self.variance + np.log(2.*np.pi*self.variance))
def _dnlog_mass_dgp(self,gp,obs): def _dnlog_mass_dgp(self,gp,obs):
return (self.gp_link.transf(gp)-obs)/np.sqrt(self.variance) * self.gp_link.dtransf_df(gp) return (self.gp_link.transf(gp)-obs)/self.variance * self.gp_link.dtransf_df(gp)
def _d2nlog_mass_dgp2(self,gp,obs): def _d2nlog_mass_dgp2(self,gp,obs):
return ((self.gp_link.transf(gp)-obs)*self.gp_link.d2transf_df2(gp) + self.gp_link.dtransf_df(gp)**2)/self.variance return ((self.gp_link.transf(gp)-obs)*self.gp_link.d2transf_df2(gp) + self.gp_link.dtransf_df(gp)**2)/self.variance

3
GPy/likelihoods/noise_models/noise_distributions.py
View file

@ -43,6 +43,9 @@ class NoiseDistribution(object):
def _set_params(self,p): def _set_params(self,p):
pass pass
def _gradients(self,partial):
return np.zeros(0)
def _preprocess_values(self,Y): def _preprocess_values(self,Y):
""" """
In case it is needed, this function assess the output values or makes any pertinent transformation on them. In case it is needed, this function assess the output values or makes any pertinent transformation on them.