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[var dtc] added code for additional covariates, not affecting normal procedures

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Max Zwiessele 2015-01-30 09:43:02 +00:00
parent 57e941140c
commit 167e9c538d
2 changed files with 4 additions and 4 deletions
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4
GPy/models/bayesian_gplvm_minibatch.py
View file

@ -83,8 +83,8 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
"""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(BayesianGPLVMMiniBatch, self)._inner_parameters_changed(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm, dL_dKmm=dL_dKmm, subset_indices=subset_indices)
def _inner_parameters_changed(self, kern, X, Z, likelihood, Y, Y_metadata, Lm=None, dL_dKmm=None, subset_indices=None, **kw):
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, **kw)
if self.has_uncertain_inputs():
current_values['meangrad'], current_values['vargrad'] = self.kern.gradients_qX_expectations(

4
GPy/models/sparse_gp_minibatch.py
View file

@ -97,7 +97,7 @@ Created on 3 Nov 2014
def has_uncertain_inputs(self):
return isinstance(self.X, VariationalPosterior)
def _inner_parameters_changed(self, kern, X, Z, likelihood, Y, Y_metadata, Lm=None, dL_dKmm=None, subset_indices=None):
def _inner_parameters_changed(self, kern, X, Z, likelihood, Y, Y_metadata, Lm=None, dL_dKmm=None, subset_indices=None, **kwargs):
"""
This is the standard part, which usually belongs in parameters_changed.
@ -117,7 +117,7 @@ Created on 3 Nov 2014
algorithm.
"""
try:
posterior, log_marginal_likelihood, grad_dict = self.inference_method.inference(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm, dL_dKmm=None)
posterior, log_marginal_likelihood, grad_dict = self.inference_method.inference(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm, dL_dKmm=None, **kwargs)
except:
posterior, log_marginal_likelihood, grad_dict = self.inference_method.inference(kern, X, Z, likelihood, Y, Y_metadata)
current_values = {}