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partial derivatives for the new likelihood framework

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James Hensman 2013-02-04 10:22:01 +00:00
parent b1e5070ed0
commit 3a61c39cb8
1 changed files with 19 additions and 6 deletions
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25
GPy/models/sparse_GP.py
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@ -114,10 +114,23 @@ class sparse_GP(GP):
self.dL_dKmm += np.dot(np.dot(self.E*sf2, self.psi2_beta_scaled) - np.dot(self.C, self.psi1VVpsi1), self.Kmmi) + 0.5*self.E # dD self.dL_dKmm += np.dot(np.dot(self.E*sf2, self.psi2_beta_scaled) - np.dot(self.C, self.psi1VVpsi1), self.Kmmi) + 0.5*self.E # dD
#the partial derivative vector for the likelihood #the partial derivative vector for the likelihood
self.partial_for_likelihood = - 0.5 * self.D*self.likelihood.precision + 0.5 * (self.likelihood.Y**2).sum(1)*self.likelihood.precision**2 #dA if self.likelihood.Nparams ==0:
self.partial_for_likelihood += 0.5 * self.D * (self.psi0*self.likelihood.precision**2 - (self.psi2*self.Kmmi[None,:,:]*self.likelihood.precision[:,None,None]**2).sum(1).sum(1)/sf2) #dB #save computation here.
#self.partial_for_likelihood += 0.5 * self.D * np.sum(self.Bi*self.A)*self.likelihood.precision #dC self.partial_for_likelihood = None
#self.partial_for_likelihood += -np.diag(np.dot((self.C - 0.5 * mdot(self.C,self.psi2_beta_scaled,self.C) ) , self.psi1VVpsi1 ))*self.likelihood.precision #dD elif self.likelihood.is_heteroscedastic:
raise NotImplementedError, "heteroscedatic derivates not implemented"
#self.partial_for_likelihood = - 0.5 * self.D*self.likelihood.precision + 0.5 * (self.likelihood.Y**2).sum(1)*self.likelihood.precision**2 #dA
#self.partial_for_likelihood += 0.5 * self.D * (self.psi0*self.likelihood.precision**2 - (self.psi2*self.Kmmi[None,:,:]*self.likelihood.precision[:,None,None]**2).sum(1).sum(1)/sf2) #dB
#self.partial_for_likelihood += 0.5 * self.D * np.sum(self.Bi*self.A)*self.likelihood.precision #dC
#self.partial_for_likelihood += -np.diag(np.dot((self.C - 0.5 * mdot(self.C,self.psi2_beta_scaled,self.C) ) , self.psi1VVpsi1 ))*self.likelihood.precision #dD
else:
#likelihood is not heterscedatic
beta = self.likelihood.precision
dbeta = 0.5 * self.N*self.D/beta - 0.5 * np.sum(np.square(self.likelihood.Y))
dbeta += - 0.5 * self.D * (self.psi0.sum() - np.trace(self.A)/beta*sf2)
dbeta += - 0.5 * self.D * np.sum(self.Bi*self.A)/beta
dbeta += np.sum((self.C - 0.5 * mdot(self.C,self.psi2_beta_scaled,self.C) ) * self.psi1VVpsi1 )/beta
self.partial_for_likelihood = -dbeta*self.likelihood.precision
def _set_params(self, p): def _set_params(self, p):
@ -195,9 +208,9 @@ class sparse_GP(GP):
def plot(self, *args, **kwargs): def plot(self, *args, **kwargs):
""" """
Plot the fitted model: just call the GP_regression plot function and then add inducing inputs Plot the fitted model: just call the GP plot function and then add inducing inputs
""" """
GP_regression.plot(self,*args,**kwargs) GP.plot(self,*args,**kwargs)
if self.Q==1: if self.Q==1:
pb.plot(self.Z,self.Z*0+pb.ylim()[0],'k|',mew=1.5,markersize=12) pb.plot(self.Z,self.Z*0+pb.ylim()[0],'k|',mew=1.5,markersize=12)
if self.has_uncertain_inputs: if self.has_uncertain_inputs: