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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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This commit is contained in:
Max Zwiessele 2014-11-05 15:38:58 +00:00
commit 7dbf1635fc
23 changed files with 715 additions and 684 deletions
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5
GPy/models/bayesian_gplvm.py
View file

@ -7,7 +7,6 @@ from ..core.sparse_gp_mpi import SparseGP_MPI
from ..likelihoods import Gaussian
from ..core.parameterization.variational import NormalPosterior, NormalPrior
from ..inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch
from ..inference.latent_function_inference.var_dtc_gpu import VarDTC_GPU
import logging
class BayesianGPLVM(SparseGP_MPI):
@ -68,14 +67,12 @@ class BayesianGPLVM(SparseGP_MPI):
if isinstance(inference_method,VarDTC_minibatch):
inference_method.mpi_comm = mpi_comm
if kernel.useGPU and isinstance(inference_method, VarDTC_GPU):
kernel.psicomp.GPU_direct = True
super(BayesianGPLVM,self).__init__(X, Y, Z, kernel, likelihood=likelihood,
name=name, inference_method=inference_method,
normalizer=normalizer, mpi_comm=mpi_comm,
variational_prior=self.variational_prior,
)
self.link_parameter(self.X, index=0)
def set_X_gradients(self, X, X_grad):
"""Set the gradients of the posterior distribution of X in its specific form."""

1
GPy/models/bayesian_gplvm_minibatch.py
View file

@ -6,7 +6,6 @@ from .. import kern
from ..likelihoods import Gaussian
from ..core.parameterization.variational import NormalPosterior, NormalPrior
from ..inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch
from ..inference.latent_function_inference.var_dtc_gpu import VarDTC_GPU
import logging
from GPy.models.sparse_gp_minibatch import SparseGPMiniBatch

12
GPy/models/sparse_gp_regression.py
View file

@ -4,12 +4,13 @@
import numpy as np
from ..core import SparseGP
from ..core.sparse_gp_mpi import SparseGP_MPI
from .. import likelihoods
from .. import kern
from ..inference.latent_function_inference import VarDTC
from ..core.parameterization.variational import NormalPosterior
class SparseGPRegression(SparseGP):
class SparseGPRegression(SparseGP_MPI):
"""
Gaussian Process model for regression
@ -48,7 +49,14 @@ class SparseGPRegression(SparseGP):
if not (X_variance is None):
X = NormalPosterior(X,X_variance)
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC(), normalizer=normalizer)
SparseGP_MPI.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC(), normalizer=normalizer)
def parameters_changed(self):
from ..inference.latent_function_inference.var_dtc_parallel import update_gradients_sparsegp,VarDTC_minibatch
if isinstance(self.inference_method,VarDTC_minibatch):
update_gradients_sparsegp(self, mpi_comm=self.mpi_comm)
else:
super(SparseGPRegression, self).parameters_changed()
class SparseGPRegressionUncertainInput(SparseGP):
"""

2
GPy/models/ss_gplvm.py
View file

@ -8,7 +8,6 @@ from .. import kern
from ..likelihoods import Gaussian
from ..core.parameterization.variational import SpikeAndSlabPrior, SpikeAndSlabPosterior
from ..inference.latent_function_inference.var_dtc_parallel import update_gradients, VarDTC_minibatch
from ..inference.latent_function_inference.var_dtc_gpu import VarDTC_GPU
from ..kern._src.psi_comp.ssrbf_psi_gpucomp import PSICOMP_SSRBF_GPU
class SSGPLVM(SparseGP_MPI):
@ -72,6 +71,7 @@ class SSGPLVM(SparseGP_MPI):
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()
self.link_parameter(self.X, index=0)
if self.group_spike:
[self.X.gamma[:,i].tie('tieGamma'+str(i)) for i in xrange(self.X.gamma.shape[1])] # Tie columns together