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

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This commit is contained in:
mzwiessele 2015-06-29 10:19:43 +02:00
commit 4ca4916cc0
28 changed files with 14715 additions and 197 deletions
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24
GPy/inference/latent_function_inference/inferenceX.py
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@ -45,17 +45,23 @@ class InferenceX(Model):
super(InferenceX, self).__init__(name)
self.likelihood = model.likelihood.copy()
self.kern = model.kern.copy()
if model.kern.useGPU:
from ...models import SSGPLVM
if isinstance(model, SSGPLVM):
self.kern.GPU_SSRBF(True)
else:
self.kern.GPU(True)
# if model.kern.useGPU:
# from ...models import SSGPLVM
# if isinstance(model, SSGPLVM):
# self.kern.GPU_SSRBF(True)
# else:
# self.kern.GPU(True)
from copy import deepcopy
self.posterior = deepcopy(model.posterior)
if hasattr(model, 'variational_prior'):
self.uncertain_input = True
self.variational_prior = model.variational_prior.copy()
from ...models.ss_gplvm import IBPPrior
from ...models.ss_mrd import IBPPrior_SSMRD
if isinstance(model.variational_prior, IBPPrior) or isinstance(model.variational_prior, IBPPrior_SSMRD):
from ...core.parameterization.variational import SpikeAndSlabPrior
self.variational_prior = SpikeAndSlabPrior(pi=05,learnPi=False, group_spike=False)
else:
self.variational_prior = model.variational_prior.copy()
else:
self.uncertain_input = False
if hasattr(model, 'inducing_inputs'):
@ -147,9 +153,9 @@ class InferenceX(Model):
from ...core.parameterization.variational import SpikeAndSlabPrior
if isinstance(self.variational_prior, SpikeAndSlabPrior):
# Update Log-likelihood
KL_div = self.variational_prior.KL_divergence(self.X, N=self.Y.shape[0])
KL_div = self.variational_prior.KL_divergence(self.X)
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X, N=self.Y.shape[0])
self.variational_prior.update_gradients_KL(self.X)
else:
# Update Log-likelihood
KL_div = self.variational_prior.KL_divergence(self.X)

2
GPy/inference/optimization/optimization.py
View file

@ -142,7 +142,7 @@ class opt_lbfgsb(Optimizer):
#a more helpful error message is available in opt_result in the Error case
if opt_result[2]['warnflag']==2:
self.status = 'Error' + opt_result[2]['task']
self.status = 'Error' + str(opt_result[2]['task'])
class opt_simplex(Optimizer):
def __init__(self, *args, **kwargs):