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[missing_data in sparse gp] can be extended towards missing_data handling in gp itself. Setting up gpy issue

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This commit is contained in:
Max Zwiessele 2014-10-09 10:34:01 +01:00
parent de801c9d29
commit 829e40b25c
5 changed files with 15 additions and 11 deletions
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11
GPy/models/bayesian_gplvm.py
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@ -32,7 +32,7 @@ class BayesianGPLVM(SparseGP_MPI):
self.__IN_OPTIMIZATION__ = False
self.logger = logging.getLogger(self.__class__.__name__)
if X == None:
if X is None:
from ..util.initialization import initialize_latent
self.logger.info("initializing latent space X with method {}".format(init))
X, fracs = initialize_latent(init, input_dim, Y)
@ -97,14 +97,19 @@ class BayesianGPLVM(SparseGP_MPI):
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:
X.mean.gradient[:] = 0
X.variance.gradient[:] = 0
self.variational_prior.update_gradients_KL(X)
current_values['meangrad'] += X.mean.gradient
current_values['vargrad'] += X.variance.gradient
value_indices['meangrad'] = subset_indices['samples']
value_indices['vargrad'] = subset_indices['samples']
if subset_indices is not None:
value_indices['meangrad'] = subset_indices['samples']
value_indices['vargrad'] = subset_indices['samples']
return posterior, log_marginal_likelihood, grad_dict, current_values, value_indices
def _outer_values_update(self, full_values):