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dL_dthetaL in missing data vardtc

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James Hensman 2014-03-17 16:38:04 +00:00
parent f2d5ee42eb
commit caf1dc2609
1 changed files with 6 additions and 4 deletions
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10
GPy/inference/latent_function_inference/var_dtc.py
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@ -65,7 +65,7 @@ class VarDTC(object):
_, output_dim = Y.shape _, output_dim = Y.shape
#see whether we've got a different noise variance for each datum #see whether we've got a different noise variance for each datum
beta = 1./np.fmax(likelihood.variance, 1e-6) beta = 1./np.fmax(likelihood.gaussian_variance(Y_metadata), 1e-6)
# VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency! # VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!
#self.YYTfactor = self.get_YYTfactor(Y) #self.YYTfactor = self.get_YYTfactor(Y)
#VVT_factor = self.get_VVTfactor(self.YYTfactor, beta) #VVT_factor = self.get_VVTfactor(self.YYTfactor, beta)
@ -221,7 +221,7 @@ class VarDTCMissingData(object):
psi2_all = None psi2_all = None
Ys, traces = self._Y(Y) Ys, traces = self._Y(Y)
beta_all = 1./np.fmax(likelihood.variance, 1e-6) beta_all = 1./np.fmax(likelihood.gaussian_variance(Y_metadata), 1e-6)
het_noise = beta_all.size != 1 het_noise = beta_all.size != 1
import itertools import itertools
@ -328,18 +328,20 @@ class VarDTCMissingData(object):
diag.add(Bi, 1) diag.add(Bi, 1)
woodbury_inv_all[:, :, ind] = backsub_both_sides(Lm, Bi)[:,:,None] woodbury_inv_all[:, :, ind] = backsub_both_sides(Lm, Bi)[:,:,None]
dL_dthetaL = likelihood.exact_inference_gradients(dL_dR)
# gradients: # gradients:
if uncertain_inputs: if uncertain_inputs:
grad_dict = {'dL_dKmm': dL_dKmm, grad_dict = {'dL_dKmm': dL_dKmm,
'dL_dpsi0':dL_dpsi0_all, 'dL_dpsi0':dL_dpsi0_all,
'dL_dpsi1':dL_dpsi1_all, 'dL_dpsi1':dL_dpsi1_all,
'dL_dpsi2':dL_dpsi2_all, 'dL_dpsi2':dL_dpsi2_all,
'dL_dR':dL_dR} 'dL_dthetaL':dL_dthetaL}
else: else:
grad_dict = {'dL_dKmm': dL_dKmm, grad_dict = {'dL_dKmm': dL_dKmm,
'dL_dKdiag':dL_dpsi0_all, 'dL_dKdiag':dL_dpsi0_all,
'dL_dKnm':dL_dpsi1_all, 'dL_dKnm':dL_dpsi1_all,
'dL_dR':dL_dR} 'dL_dthetaL':dL_dthetaL}
#get sufficient things for posterior prediction #get sufficient things for posterior prediction
#TODO: do we really want to do this in the loop? #TODO: do we really want to do this in the loop?