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adapt sparsegp_mpi for normalizer arguement

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This commit is contained in:
Zhenwen Dai 2014-08-28 18:01:25 +01:00
parent 91d1cd3131
commit 313a238b15
2 changed files with 4 additions and 4 deletions
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4
GPy/core/sparse_gp_mpi.py
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@ -38,7 +38,7 @@ class SparseGP_MPI(SparseGP):
"""
def __init__(self, X, Y, Z, kernel, likelihood, variational_prior=None, inference_method=None, name='sparse gp mpi', Y_metadata=None, mpi_comm=None):
def __init__(self, X, Y, Z, kernel, likelihood, variational_prior=None, inference_method=None, name='sparse gp mpi', Y_metadata=None, mpi_comm=None, normalizer=False):
self._IN_OPTIMIZATION_ = False
if mpi_comm != None:
if inference_method is None:
@ -46,7 +46,7 @@ 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)
super(SparseGP_MPI, self).__init__(X, Y, Z, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata, normalizer=normalizer)
self.updates = False
self.add_parameter(self.X, index=0)
if variational_prior is not None:

4
GPy/models/ss_gplvm.py
View file

@ -24,7 +24,7 @@ class SSGPLVM(SparseGP_MPI):
"""
def __init__(self, Y, input_dim, X=None, X_variance=None, Gamma=None, init='PCA', num_inducing=10,
Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike_and_Slab GPLVM', group_spike=False, mpi_comm=None, pi=None, learnPi=True, **kwargs):
Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike_and_Slab GPLVM', group_spike=False, mpi_comm=None, pi=None, learnPi=True,normalizer=False, **kwargs):
self.group_spike = group_spike
@ -69,7 +69,7 @@ class SSGPLVM(SparseGP_MPI):
X = SpikeAndSlabPosterior(X, X_variance, gamma)
super(SSGPLVM,self).__init__(X, Y, Z, kernel, likelihood, variational_prior=self.variational_prior, inference_method=inference_method, name=name, mpi_comm=mpi_comm, **kwargs)
super(SSGPLVM,self).__init__(X, Y, Z, kernel, likelihood, variational_prior=self.variational_prior, inference_method=inference_method, name=name, mpi_comm=mpi_comm, normalizer=normalizer, **kwargs)
self.X.unfix()
self.X.variance.constrain_positive()