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[minbatch var dtc] adjustments to bgplvm minibatch

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Max Zwiessele 2014-11-03 14:19:03 +00:00
parent 6b3888f163
commit d0a5420f2f
2 changed files with 17 additions and 51 deletions
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64
GPy/models/bayesian_gplvm.py
View file

@ -75,8 +75,7 @@ class BayesianGPLVM(SparseGP_MPI):
name=name, inference_method=inference_method,
normalizer=normalizer, mpi_comm=mpi_comm,
variational_prior=self.variational_prior,
missing_data=missing_data, stochastic=stochastic,
batchsize=batchsize)
)
def set_X_gradients(self, X, X_grad):
"""Set the gradients of the posterior distribution of X in its specific form."""
@ -86,55 +85,22 @@ class BayesianGPLVM(SparseGP_MPI):
"""Get the gradients of the posterior distribution of X in its specific form."""
return X.mean.gradient, X.variance.gradient
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(BayesianGPLVM, self)._inner_parameters_changed(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm, dL_dKmm=dL_dKmm, subset_indices=subset_indices)
kl_fctr = 1.
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:
X.mean.gradient[:] = 0
X.variance.gradient[:] = 0
self.variational_prior.update_gradients_KL(X)
if self.missing_data:
current_values['meangrad'] += kl_fctr*X.mean.gradient/d
current_values['vargrad'] += kl_fctr*X.variance.gradient/d
else:
current_values['meangrad'] += kl_fctr*X.mean.gradient
current_values['vargrad'] += kl_fctr*X.variance.gradient
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):
"""
Here you put the values, which were collected before in the right places.
E.g. set the gradients of parameters, etc.
"""
super(BayesianGPLVM, self)._outer_values_update(full_values)
self.X.mean.gradient = full_values['meangrad']
self.X.variance.gradient = full_values['vargrad']
def _outer_init_full_values(self):
return dict(meangrad=np.zeros(self.X.mean.shape),
vargrad=np.zeros(self.X.variance.shape))
def parameters_changed(self):
super(BayesianGPLVM,self).parameters_changed()
kl_fctr = 1.
self._log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(self.X)
self.X.mean.gradient, self.X.variance.gradient = self.kern.gradients_qX_expectations(
variational_posterior=self.X,
Z=self.Z,
dL_dpsi0=self.grad_dict['dL_dpsi0'],
dL_dpsi1=self.grad_dict['dL_dpsi1'],
dL_dpsi2=self.grad_dict['dL_dpsi2'])
self.variational_prior.update_gradients_KL(self.X)
if isinstance(self.inference_method, VarDTC_minibatch):
return

4
GPy/models/bayesian_gplvm_minibatch.py
View file

@ -26,8 +26,6 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
Z=None, kernel=None, inference_method=None, likelihood=None,
name='bayesian gplvm', normalizer=None,
missing_data=False, stochastic=False, batchsize=1):
self.__IN_OPTIMIZATION__ = False
self.logger = logging.getLogger(self.__class__.__name__)
if X is None:
from ..util.initialization import initialize_latent
@ -70,6 +68,8 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
normalizer=normalizer,
missing_data=missing_data, stochastic=stochastic,
batchsize=batchsize)
self.X = X
self.link_parameter(self.X, 0)
def set_X_gradients(self, X, X_grad):
"""Set the gradients of the posterior distribution of X in its specific form."""