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[stochastics] update for new stochastic iptimizers in gpy

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This commit is contained in:
mzwiessele 2016-03-08 09:47:24 +00:00
parent c72d085e2b
commit e4a4344334
4 changed files with 152 additions and 2 deletions
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12
GPy/models/sparse_gp_minibatch.py
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@ -41,6 +41,7 @@ class SparseGPMiniBatch(SparseGP):
def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None,
name='sparse gp', Y_metadata=None, normalizer=False,
missing_data=False, stochastic=False, batchsize=1):
self._update_stochastics = False
# pick a sensible inference method
if inference_method is None:
@ -73,7 +74,14 @@ class SparseGPMiniBatch(SparseGP):
logger.info("Adding Z as parameter")
self.link_parameter(self.Z, index=0)
self.posterior = None
def optimize(self, optimizer=None, start=None, **kwargs):
try:
self._update_stochastics = True
SparseGP.optimize(self, optimizer=optimizer, start=start, **kwargs)
finally:
self._update_stochastics = False
def has_uncertain_inputs(self):
return isinstance(self.X, VariationalPosterior)
@ -314,6 +322,8 @@ class SparseGPMiniBatch(SparseGP):
if self.missing_data:
self._outer_loop_for_missing_data()
elif self.stochastics:
if self._update_stochastics:
self.stochastics.do_stochastics()
self._outer_loop_without_missing_data()
else:
self.posterior, self._log_marginal_likelihood, self.grad_dict = self._inner_parameters_changed(self.kern, self.X, self.Z, self.likelihood, self.Y_normalized, self.Y_metadata)