apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-10 04:22:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

[MRD] init and sim nicer now

Browse source
This commit is contained in:
Max Zwiessele 2014-11-12 11:34:44 +00:00
parent 9b2e49c949
commit a5c3e88f83
4 changed files with 67 additions and 66 deletions
Download patch file Download diff file Expand all files Collapse all files

13
GPy/models/bayesian_gplvm_minibatch.py
View file

@ -81,18 +81,19 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
def _inner_parameters_changed(self, kern, X, Z, likelihood, Y, Y_metadata, Lm=None, dL_dKmm=None, subset_indices=None):
posterior, log_marginal_likelihood, grad_dict, current_values, value_indices = super(BayesianGPLVMMiniBatch, self)._inner_parameters_changed(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm, dL_dKmm=dL_dKmm, subset_indices=subset_indices)
kl_fctr = 1.
current_values['meangrad'], current_values['vargrad'] = self.kern.gradients_qX_expectations(
variational_posterior=X,
Z=Z, dL_dpsi0=grad_dict['dL_dpsi0'],
dL_dpsi1=grad_dict['dL_dpsi1'],
dL_dpsi2=grad_dict['dL_dpsi2'])
kl_fctr = self.kl_factr
if self.missing_data:
d = self.output_dim
log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(X)/d
else:
log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(X)
current_values['meangrad'], current_values['vargrad'] = self.kern.gradients_qX_expectations(
variational_posterior=X,
Z=Z, dL_dpsi0=grad_dict['dL_dpsi0'],
dL_dpsi1=grad_dict['dL_dpsi1'],
dL_dpsi2=grad_dict['dL_dpsi2'])
# Subsetting Variational Posterior objects, makes the gradients
# empty. We need them to be 0 though: