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Merge branch 'devel' into minibatch

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This commit is contained in:
mzwiessele 2016-03-07 13:42:24 +00:00
commit 7d2ffcde20
50 changed files with 124 additions and 73 deletions
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2
GPy/models/bayesian_gplvm.py
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@ -61,7 +61,7 @@ class BayesianGPLVM(SparseGP_MPI):
else:
from ..inference.latent_function_inference.var_dtc import VarDTC
self.logger.debug("creating inference_method var_dtc")
inference_method = VarDTC(limit=1 if not missing_data else Y.shape[1])
inference_method = VarDTC(limit=3 if not missing_data else Y.shape[1])
if isinstance(inference_method,VarDTC_minibatch):
inference_method.mpi_comm = mpi_comm

4
GPy/models/bayesian_gplvm_minibatch.py
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@ -61,7 +61,7 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
if inference_method is None:
from ..inference.latent_function_inference.var_dtc import VarDTC
self.logger.debug("creating inference_method var_dtc")
inference_method = VarDTC(limit=1 if not missing_data else Y.shape[1])
inference_method = VarDTC(limit=3 if not missing_data else Y.shape[1])
super(BayesianGPLVMMiniBatch,self).__init__(X, Y, Z, kernel, likelihood=likelihood,
name=name, inference_method=inference_method,
@ -126,4 +126,4 @@ 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()
self._Xgrad = self.X.gradient.copy()

2
GPy/models/gplvm.py
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@ -41,4 +41,4 @@ class GPLVM(GP):
def parameters_changed(self):
super(GPLVM, self).parameters_changed()
self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X, None)
self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X, None)

2
GPy/models/sparse_gp_minibatch.py
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@ -45,7 +45,7 @@ class SparseGPMiniBatch(SparseGP):
# pick a sensible inference method
if inference_method is None:
if isinstance(likelihood, likelihoods.Gaussian):
inference_method = var_dtc.VarDTC(limit=1 if not missing_data else Y.shape[1])
inference_method = var_dtc.VarDTC(limit=3 if not missing_data else Y.shape[1])
else:
#inference_method = ??
raise NotImplementedError("what to do what to do?")

2
GPy/models/sparse_gp_regression.py
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@ -62,4 +62,4 @@ class SparseGPRegression(SparseGP_MPI):
if isinstance(self.inference_method,VarDTC_minibatch):
update_gradients_sparsegp(self, mpi_comm=self.mpi_comm)
else:
super(SparseGPRegression, self).parameters_changed()
super(SparseGPRegression, self).parameters_changed()