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Add mean function functionality to dtc inference method

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Mark Pullin 2017-11-13 21:15:38 +00:00
parent 754c67f71d
commit a24a9b3edc
5 changed files with 45 additions and 18 deletions
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8
GPy/core/sparse_gp.py
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@ -74,11 +74,16 @@ class SparseGP(GP):
if trigger_update: self.update_model(True)
def parameters_changed(self):
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y, self.Y_metadata)
self.posterior, self._log_marginal_likelihood, self.grad_dict = \
self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood,
self.Y, Y_metadata=self.Y_metadata,
mean_function=self.mean_function)
self._update_gradients()
def _update_gradients(self):
self.likelihood.update_gradients(self.grad_dict['dL_dthetaL'])
if self.mean_function is not None:
self.mean_function.update_gradients(self.grad_dict['dL_dm'], self.X)
if isinstance(self.X, VariationalPosterior):
#gradients wrt kernel
@ -112,4 +117,3 @@ class SparseGP(GP):
self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X)
self._Zgrad = self.Z.gradient.copy()

11
GPy/core/sparse_gp_mpi.py
View file

@ -34,7 +34,9 @@ class SparseGP_MPI(SparseGP):
"""
def __init__(self, X, Y, Z, kernel, likelihood, variational_prior=None, inference_method=None, name='sparse gp', Y_metadata=None, mpi_comm=None, normalizer=False):
def __init__(self, X, Y, Z, kernel, likelihood, variational_prior=None,
mean_function=None, inference_method=None, name='sparse gp',
Y_metadata=None, mpi_comm=None, normalizer=False):
self._IN_OPTIMIZATION_ = False
if mpi_comm != None:
if inference_method is None:
@ -42,12 +44,12 @@ class SparseGP_MPI(SparseGP):
else:
assert isinstance(inference_method, VarDTC_minibatch), 'inference_method has to support MPI!'
super(SparseGP_MPI, self).__init__(X, Y, Z, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata, normalizer=normalizer)
super(SparseGP_MPI, self).__init__(X, Y, Z, kernel, likelihood, inference_method=inference_method, mean_function=mean_function, name=name, Y_metadata=Y_metadata, normalizer=normalizer)
self.update_model(False)
if variational_prior is not None:
self.link_parameter(variational_prior)
self.mpi_comm = mpi_comm
# Manage the data (Y) division
if mpi_comm != None:
@ -118,4 +120,3 @@ class SparseGP_MPI(SparseGP):
update_gradients(self, mpi_comm=self.mpi_comm)
else:
super(SparseGP_MPI,self).parameters_changed()