apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-03 08:42:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

changes to DTC

Browse source
This commit is contained in:
James Hensman 2014-02-11 20:05:36 +00:00
parent 1b29c0939e
commit 54b7581c39
2 changed files with 8 additions and 8 deletions
Download patch file Download diff file Expand all files Collapse all files

12
GPy/inference/latent_function_inference/dtc.py
View file

@ -52,20 +52,20 @@ class DTC(object):
b, _ = dtrtrs(LA, tmp*beta, lower=1)
tmp, _ = dtrtrs(LA, b, lower=1, trans=1)
v, _ = dtrtrs(L, tmp, lower=1, trans=1)
tmp = tdrtrs(LA, Li, lower=1, trans=0)
tmp, _ = dtrtrs(LA, Li, lower=1, trans=0)
P = tdot(tmp.T)
#compute log marginal
log_marginal = -0.5*num_data*output_dim*np.log(2*np.pi) + \
-np.sum(np.log(np.diag(LA)))*output_dim + \
0.5*num_data*output_dim*np.log(beta) + \
-0.5*beta*np.sum(np.square(Y)) +
-0.5*beta*np.sum(np.square(Y)) + \
0.5*np.sum(np.square(b))
# Compute dL_dKmm
tmp, _ = dtrtrs(L, A_I, lower=1, trans=1)
dL_dK, _ = dtrtrs(L, tmp.T, lower=1, trans=0)
tmp, _ = dtrtrs(LA, tmp.T. lower=1, trans=1)
tmp, _ = dtrtrs(LA, tmp.T, lower=1, trans=1)
dL_dK -= tdot(tmp.T)
dL_dK *= output_dim
dL_dK -= tdot(v)
@ -79,17 +79,17 @@ class DTC(object):
#compute dL_dR
Uv = np.dot(U, v)
dL_dR = 0.5*(np.sum(U*np.dot(P, U.T), 1) - beta * np.sum(np.square(Y, 1)) - 2.*np.sum(Uv*Y, 1) + np.sum(np.square(Uv), 1)
dL_dR = 0.5*(np.sum(U*np.dot(U,P), 1) - beta * np.sum(np.square(Y), 1) - 2.*np.sum(Uv*Y, 1) + np.sum(np.square(Uv), 1)
)*beta**2
grad_dict = {'dL_dKmm': dL_dKmm, 'dL_dKdiag':np.zeros_like(Knn), 'dL_dKnm':dL_dU}
grad_dict = {'dL_dKmm': dL_dK, 'dL_dKdiag':np.zeros_like(Knn), 'dL_dKnm':dL_dU.T}
#update gradients
kern.update_gradients_sparse(X=X, Z=Z, **grad_dict)
likelihood.update_gradients(dL_dR)
#construct a posterior object
post = Posterior(woodbury_inv=Kmmi-P, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=Lm)
post = Posterior(woodbury_inv=Kmmi-P, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=L)
return post, log_marginal, grad_dict