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gradients now working in uncollapsed GP

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James Hensman 2012-12-16 14:48:24 +00:00
parent 053ebbe2d4
commit 35a7e6179d
1 changed files with 6 additions and 5 deletions
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11
GPy/models/uncollapsed_sparse_GP.py
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@ -36,12 +36,12 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
self.D = Y.shape[1] self.D = Y.shape[1]
if q_u is None: if q_u is None:
if 'Z' in kwargs.keys(): if 'Z' in kwargs.keys():
self.M = Z.shape[0] self.M = kwargs['Z'].shape[0]
else: else:
self.M = M self.M = M
q_u = np.hstack((np.random.randn(self.M*self.D),-0.5*np.eye(self.M).flatten())) q_u = np.hstack((np.random.randn(self.M*self.D),-0.5*np.eye(self.M).flatten()))
self.set_vb_param(q_u) self.set_vb_param(q_u)
sparse_GP_regression.__init__(self, X, Y, M=M,*args, **kwargs) sparse_GP_regression.__init__(self, X, Y, M=self.M,*args, **kwargs)
def _computations(self): def _computations(self):
self.V = self.beta*self.Y self.V = self.beta*self.Y
@ -57,12 +57,13 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
# Compute dL_dpsi # Compute dL_dpsi
self.dL_dpsi0 = - 0.5 * self.D * self.beta * np.ones(self.N) self.dL_dpsi0 = - 0.5 * self.D * self.beta * np.ones(self.N)
self.dL_dpsi1 = np.dot(self.VmT,self.Kmmi).T*0 # This is the correct term for E I think... self.dL_dpsi1 = np.dot(self.VmT,self.Kmmi).T # This is the correct term for E I think...
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*self.D*self.psi2 -0.5*self.D*self.Kmm +0.5*self.D*(self.q_u_expectation[1]) + 0.5*self.beta*mdot(self.psi2,self.Kmmi,self.q_u_expectation[1]) + 0.5*self.beta*mdot(self.q_u_expectation[1],self.Kmmi, self.psi2) tmp = -0.5*self.beta*self.D*self.psi2 -0.5*self.D*self.Kmm +0.5*self.D*(self.q_u_expectation[1]) + 0.5*self.beta*mdot(self.psi2,self.Kmmi,self.q_u_expectation[1]) + 0.5*self.beta*mdot(self.q_u_expectation[1],self.Kmmi, self.psi2)
#tmp = - np.dot(self.q_u_expectation[0],self.psi1V.T) tmptmp = - np.dot(self.q_u_expectation[0],self.psi1V.T)*0.5
tmp += tmptmp + tmptmp.T
self.dL_dKmm = mdot(self.Kmmi,tmp,self.Kmmi) self.dL_dKmm = mdot(self.Kmmi,tmp,self.Kmmi)
def log_likelihood(self): def log_likelihood(self):
@ -74,7 +75,7 @@ class uncollapsed_sparse_GP(sparse_GP_regression):
C = -0.5*self.D *(self.Kmm_logdet-self.q_u_logdet + np.sum(self.Lambda * self.q_u_expectation[1]) - self.M) C = -0.5*self.D *(self.Kmm_logdet-self.q_u_logdet + np.sum(self.Lambda * self.q_u_expectation[1]) - self.M)
D = -0.5*self.beta*self.trYYT D = -0.5*self.beta*self.trYYT
E = np.sum(np.dot(self.V.T,self.projected_mean)) E = np.sum(np.dot(self.V.T,self.projected_mean))
return A+B+C+D return A+B+C+D+E
def dL_dbeta(self): def dL_dbeta(self):
""" """