apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-08 11:32:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

some gradient tidying and a small correction in the natural gradients

Browse source
This commit is contained in:
James Hensman 2012-12-16 19:03:08 +00:00
parent 43af1c174d
commit b56de4762e
1 changed files with 4 additions and 6 deletions
Download patch file Download diff file Expand all files Collapse all files

10
GPy/models/uncollapsed_sparse_GP.py
View file

@ -61,12 +61,10 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
self.dL_dpsi2 = 0.5 * self.beta * self.D * (self.Kmmi - mdot(self.Kmmi,self.q_u_expectation[1],self.Kmmi)) self.dL_dpsi2 = 0.5 * self.beta * self.D * (self.Kmmi - mdot(self.Kmmi,self.q_u_expectation[1],self.Kmmi))
# Compute dL_dKmm # Compute dL_dKmm
tmp = 0.5*self.beta*mdot(self.psi2,self.Kmmi,self.q_u_expectation[1]) tmp = self.beta*mdot(self.psi2,self.Kmmi,self.q_u_expectation[1]) -np.dot(self.q_u_expectation[0],self.psi1V.T)
tmp += tmp.T tmp += tmp.T
tmp += 0.5*self.D*(-self.beta*self.psi2 - self.Kmm + self.q_u_expectation[1]) tmp += self.D*(-self.beta*self.psi2 - self.Kmm + self.q_u_expectation[1])
tmptmp = - 0.5*np.dot(self.q_u_expectation[0],self.psi1V.T) self.dL_dKmm = 0.5*mdot(self.Kmmi,tmp,self.Kmmi)
tmp += tmptmp + tmptmp.T
self.dL_dKmm = mdot(self.Kmmi,tmp,self.Kmmi)
def log_likelihood(self): def log_likelihood(self):
""" """
@ -127,7 +125,7 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
Note that the natural gradient in either is given by the gradient in the other (See Hensman et al 2012 Fast Variational inference in the conjugate exponential Family) Note that the natural gradient in either is given by the gradient in the other (See Hensman et al 2012 Fast Variational inference in the conjugate exponential Family)
""" """
dL_dmmT_S = -0.5*self.Lambda+self.q_u_canonical[1] dL_dmmT_S = -0.5*self.Lambda-self.q_u_canonical[1]
dL_dm = np.dot(self.Kmmi,self.psi1V) - self.q_u_canonical[0] dL_dm = np.dot(self.Kmmi,self.psi1V) - self.q_u_canonical[0]
#dL_dSim = #dL_dSim =