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This commit is contained in:
Zhenwen Dai 2014-05-08 13:08:16 +01:00
parent 06336bf0d4
commit ddbf15d763
6 changed files with 122 additions and 86 deletions
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56
GPy/inference/latent_function_inference/var_dtc_parallel.py
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@ -116,7 +116,7 @@ class VarDTC_minibatch(object):
if het_noise:
psi2_full += beta_slice*np.outer(psi1,psi1)
else:
psi2_full += np.outer(psi1,psi1)
psi2_full += np.outer(psi1.T,psi1.T)
if not het_noise:
psi0_full *= beta
@ -169,13 +169,16 @@ class VarDTC_minibatch(object):
#======================================================================
# Compute the Posterior distribution of inducing points p(u|Y)
#======================================================================
# phi_u_mean = np.dot(Kmm,v)
# LLInvKmm,_ = dtrtrs(LL,Kmm)
# # phi_u_var = np.einsum('ma,mb->ab',LLInvKmm,LLInvKmm)
# phi_u_var = Kmm - np.dot(LLInvKmm.T,LLInvKmm)
post = Posterior(woodbury_inv=KmmInvPsi2P, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=Lm)
#======================================================================
# Compute dL_dthetaL for uncertian input and non-heter noise
#======================================================================
if uncertain_inputs and not het_noise:
dL_dthetaL = (YRY_full*beta + beta*output_dim*psi0_full - num_data*output_dim*beta)/2. - beta*(dL_dpsi2R*psi2_full).sum() - beta*(v.T*psi1Y_full).sum()
self.midRes['dL_dthetaL'] = dL_dthetaL
return logL, dL_dKmm, post
@ -213,20 +216,21 @@ class VarDTC_minibatch(object):
Y_slice = YYT_factor[n_start:n_end]
X_slice = X[n_start:n_end]
if uncertain_inputs:
psi0 = kern.psi0(Z, X_slice)
psi1 = kern.psi1(Z, X_slice)
psi2 = kern.psi2(Z, X_slice)
else:
if not uncertain_inputs:
psi0 = kern.Kdiag(X_slice)
psi1 = kern.K(X_slice, Z)
psi2 = None
betapsi1 = np.einsum('n,nm->nm',beta,psi1)
elif het_noise:
psi0 = kern.psi0(Z, X_slice)
psi1 = kern.psi1(Z, X_slice)
psi2 = kern.psi2(Z, X_slice)
betapsi1 = np.einsum('n,nm->nm',beta,psi1)
if het_noise:
beta = beta[n_start] # assuming batchsize==1
betaY = beta*Y_slice
betapsi1 = np.einsum('n,nm->nm',beta,psi1)
#======================================================================
# Load Intermediate Results
@ -234,12 +238,12 @@ class VarDTC_minibatch(object):
dL_dpsi2R = self.midRes['dL_dpsi2R']
v = self.midRes['v']
#======================================================================
# Compute dL_dpsi
#======================================================================
dL_dpsi0 = -0.5 * output_dim * (beta * np.ones((n_end-n_start,)))
dL_dpsi0 = -output_dim * (beta * np.ones((n_end-n_start,)))/2.
dL_dpsi1 = np.dot(betaY,v.T)
@ -254,20 +258,22 @@ class VarDTC_minibatch(object):
#======================================================================
if het_noise:
if uncertain_inputs:
psiR = np.einsum('mo,nmo->n',dL_dpsi2R,psi2)
else:
psiR = np.einsum('nm,no,mo->n',psi1,psi1,dL_dpsi2R)
dL_dthetaL = ((np.square(betaY)).sum(axis=-1) + np.square(beta)*(output_dim*psi0)-output_dim*beta)/2. - np.square(beta)*psiR- (betaY*np.dot(betapsi1,v)).sum(axis=-1)
else:
if uncertain_inputs:
psiR = np.einsum('mo,mo->',dL_dpsi2R,psi2)
else:
psiR = np.einsum('nm,no,mo->',psi1,psi1,dL_dpsi2R)
dL_dthetaL = ((np.square(betaY)).sum() + beta*beta*output_dim*(psi0.sum())-num_slice*output_dim*beta)/2. - beta*beta*psiR- (betaY*np.dot(betapsi1,v)).sum()
dL_dthetaL = ((np.square(betaY)).sum(axis=-1) + np.square(beta)*(output_dim*psi0)-output_dim*beta)/2. - np.square(beta)*psiR- (betaY*np.dot(betapsi1,v)).sum(axis=-1)
else:
if uncertain_inputs:
if isEnd:
dL_dthetaL = self.midRes['dL_dthetaL']
else:
dL_dthetaL = 0.
else:
psiR = np.einsum('nm,no,mo->',psi1,psi1,dL_dpsi2R)
dL_dthetaL = ((np.square(betaY)).sum() + beta*beta*output_dim*(psi0.sum())-num_slice*output_dim*beta)/2. - beta*beta*psiR- (betaY*np.dot(betapsi1,v)).sum()
if uncertain_inputs:
grad_dict = {'dL_dpsi0':dL_dpsi0,
'dL_dpsi1':dL_dpsi1,
@ -320,7 +326,7 @@ def update_gradients(model):
dL_dthetaL[n_range[0]:n_range[1]] = grad_dict['dL_dthetaL']
else:
dL_dthetaL += grad_dict['dL_dthetaL']
# Set the gradients w.r.t. kernel
model.kern.gradient = kern_grad