apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-10 12:32:40 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Added Z_tilde contribution for EP, now log_marginal is correct, need to check for var_dtc case

Browse source
This commit is contained in:
Alan Saul 2015-10-22 16:19:27 +01:00
parent 5b4abf4c34
commit 3dba1c05de
3 changed files with 28 additions and 8 deletions
Download patch file Download diff file Expand all files Collapse all files

22
GPy/inference/latent_function_inference/expectation_propagation.py
View file

@ -51,7 +51,7 @@ class EP(EPBase, ExactGaussianInference):
#if we've already run EP, just use the existing approximation stored in self._ep_approximation
mu, Sigma, mu_tilde, tau_tilde, Z_tilde = self._ep_approximation
return super(EP, self).inference(kern, X, likelihood, mu_tilde[:,None], mean_function=mean_function, Y_metadata=Y_metadata, precision=1./tau_tilde, K=K, Z=np.log(Z_tilde).sum())
return super(EP, self).inference(kern, X, likelihood, mu_tilde[:,None], mean_function=mean_function, Y_metadata=Y_metadata, precision=1./tau_tilde, K=K, Z_tilde=np.log(Z_tilde).sum())
def expectation_propagation(self, K, Y, likelihood, Y_metadata):
@ -63,6 +63,10 @@ class EP(EPBase, ExactGaussianInference):
Sigma = K.copy()
diag.add(Sigma, 1e-7)
# Makes computing the sign quicker if we work with numpy arrays rather
# than ObsArrays
Y = Y.values.copy()
#Initial values - Marginal moments
Z_hat = np.empty(num_data,dtype=np.float64)
mu_hat = np.empty(num_data,dtype=np.float64)
@ -91,15 +95,25 @@ class EP(EPBase, ExactGaussianInference):
#Cavity distribution parameters
tau_cav[i] = 1./Sigma[i,i] - self.eta*tau_tilde[i]
v_cav[i] = mu[i]/Sigma[i,i] - self.eta*v_tilde[i]
if Y_metadata is not None:
# Pick out the relavent metadata for Yi
Y_metadata_i = {}
for key in Y_metadata.keys():
Y_metadata_i[key] = Y_metadata[key][i, :]
else:
Y_metadata_i = None
#Marginal moments
Z_hat[i], mu_hat[i], sigma2_hat[i] = likelihood.moments_match_ep(Y[i], tau_cav[i], v_cav[i])#, Y_metadata=None)#=(None if Y_metadata is None else Y_metadata[i]))
Z_hat[i], mu_hat[i], sigma2_hat[i] = likelihood.moments_match_ep(Y[i], tau_cav[i], v_cav[i], Y_metadata_i=Y_metadata_i)
#Site parameters update
delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma[i,i])
delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma[i,i])
tau_tilde[i] += delta_tau
v_tilde[i] += delta_v
#Posterior distribution parameters update
DSYR(Sigma, Sigma[:,i].copy(), -delta_tau/(1.+ delta_tau*Sigma[i,i]))
# DSYR(Sigma, Sigma[:,i].copy(), -delta_tau/(1.+ delta_tau*Sigma[i,i]))
# mu = np.dot(Sigma, v_tilde)
ci = delta_tau/(1.+ delta_tau*Sigma[i,i])
DSYR(Sigma, Sigma[:,i].copy(), -ci)
mu = np.dot(Sigma, v_tilde)
#(re) compute Sigma and mu using full Cholesky decompy
@ -150,7 +164,7 @@ class EPDTC(EPBase, VarDTC):
Y_metadata=Y_metadata,
precision=tau_tilde,
Lm=Lm, dL_dKmm=dL_dKmm,
psi0=psi0, psi1=psi1, psi2=psi2, Z=Z_tilde)
psi0=psi0, psi1=psi1, psi2=psi2, Z_tilde=np.log(Z_tilde).sum())
def expectation_propagation(self, Kmm, Kmn, Y, likelihood, Y_metadata):
num_data, output_dim = Y.shape