apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-09 12:02:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

[MRD] fixed mrd for new structure

Browse source
This commit is contained in:
Max Zwiessele 2015-09-04 16:43:26 +01:00
parent 41cc9c62f5
commit 87ee4ae548
2 changed files with 10 additions and 10 deletions
Download patch file Download diff file Expand all files Collapse all files

5
GPy/models/bayesian_gplvm_minibatch.py
View file

@ -107,8 +107,7 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
self.X.gradient += self.kern.gradients_X_diag(full_values['dL_dKdiag'], self.X)
def _outer_init_full_values(self):
full_values = super(BayesianGPLVMMiniBatch, self)._outer_init_full_values()
return full_values
return super(BayesianGPLVMMiniBatch, self)._outer_init_full_values()
def parameters_changed(self):
super(BayesianGPLVMMiniBatch,self).parameters_changed()
@ -134,6 +133,8 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
d = self.output_dim
self._log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(self.X)*self.stochastics.batchsize/d
self._Xgrad = self.X.gradient.copy()
def plot_latent(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,
fignum=None, plot_inducing=True, legend=True,

15
GPy/models/mrd.py
View file

@ -174,16 +174,15 @@ class MRD(BayesianGPLVMMiniBatch):
#grad_dict = b.full_values
if self.has_uncertain_inputs():
self.X.mean.gradient += b.X.mean.gradient
self.X.variance.gradient += b.X.variance.gradient
self.X.gradient += b._Xgrad
else:
self.X.gradient += b.X.gradient
self.X.gradient += b._Xgrad
if self.has_uncertain_inputs():
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X)
self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
pass
#if self.has_uncertain_inputs():
# # update for the KL divergence
# self.variational_prior.update_gradients_KL(self.X)
# self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
# pass
def log_likelihood(self):
return self._log_marginal_likelihood