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lots of fixes, including prediction being mean and variance only

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This commit is contained in:
James Hensman 2014-03-13 14:42:03 +00:00
parent 365b8ae1e1
commit cc96f5b3d5
13 changed files with 118 additions and 128 deletions
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13
GPy/inference/latent_function_inference/dtc.py
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@ -40,7 +40,7 @@ class DTC(object):
U = Knm
Uy = np.dot(U.T,Y)
#factor Kmm
#factor Kmm
Kmmi, L, Li, _ = pdinv(Kmm)
# Compute A
@ -78,7 +78,9 @@ class DTC(object):
Uv = np.dot(U, v)
dL_dR = 0.5*(np.sum(U*np.dot(U,P), 1) - 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_dK, 'dL_dKdiag':np.zeros_like(Knn), 'dL_dKnm':dL_dU.T}
dL_dthetaL = likelihood.exact_inference_gradients(dL_dR)
grad_dict = {'dL_dKmm': dL_dK, 'dL_dKdiag':np.zeros_like(Knn), 'dL_dKnm':dL_dU.T, 'dL_dthetaL':dL_dthetaL}
#construct a posterior object
post = Posterior(woodbury_inv=Kmmi-P, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=L)
@ -154,11 +156,8 @@ class vDTC(object):
dL_dR = 0.5*(np.sum(U*np.dot(U,P), 1) - 1./beta + np.sum(np.square(Y), 1) - 2.*np.sum(Uv*Y, 1) + np.sum(np.square(Uv), 1) )*beta**2
dL_dR -=beta*trace_term/num_data
grad_dict = {'dL_dKmm': dL_dK, 'dL_dKdiag':np.zeros_like(Knn) + -0.5*beta, 'dL_dKnm':dL_dU.T}
#update gradients
kern.update_gradients_sparse(X=X, Z=Z, **grad_dict)
likelihood.update_gradients(dL_dR)
dL_dthetaL = likelihood.exact_inference_gradients(dL_dR)
grad_dict = {'dL_dKmm': dL_dK, 'dL_dKdiag':np.zeros_like(Knn) + -0.5*beta, 'dL_dKnm':dL_dU.T, 'dL_dthetaL':dL_dthetaL}
#construct a posterior object
post = Posterior(woodbury_inv=Kmmi-P, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=L)